{"id":"https://openalex.org/W7215115154","doi":"https://doi.org/10.31223/x5tr4z","title":"Preserving Probabilistic Meaning in Weather-to-Text Generation","display_name":"Preserving Probabilistic Meaning in Weather-to-Text Generation","publication_year":2026,"publication_date":"2026-10-01","ids":{"openalex":"https://openalex.org/W7215115154","doi":"https://doi.org/10.31223/x5tr4z"},"language":"en","primary_location":{"id":"doi:10.31223/x5tr4z","doi":"https://doi.org/10.31223/x5tr4z","is_oa":true,"landing_page_url":"https://doi.org/10.31223/x5tr4z","pdf_url":"https://eartharxiv.org/repository/object/15308/download/26528/","source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://eartharxiv.org/repository/object/15308/download/26528/","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125753352","display_name":"Wanfeng Ding","orcid":"https://orcid.org/0009-0000-0258-5872","observed_orcids":["https://orcid.org/0009-0000-0258-5872"]},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wanfeng Ding","raw_affiliation_strings":["Monash Climate Change Communications Hub , Monash University , Melbourne , Australia"],"raw_orcid":"https://orcid.org/0009-0000-0258-5872","affiliations":[{"raw_affiliation_string":"Monash Climate Change Communications Hub , Monash University , Melbourne , Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5153745304","display_name":"Pengxin Lin","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I141301092","display_name":"China Meteorological Administration","ror":"https://ror.org/00bx3rb98","country_code":"CN","type":"government","lineage":["https://openalex.org/I141301092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengxin Lin","raw_affiliation_strings":["JiaoLing Meteorological Service , Guangdong Meteorological Service , Meizhou , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JiaoLing Meteorological Service , Guangdong Meteorological Service , Meizhou , China","institution_ids":["https://openalex.org/I141301092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035857705","display_name":"Hanif Bhuiyan","orcid":"https://orcid.org/0000-0002-6064-8525","observed_orcids":["https://orcid.org/0000-0002-6064-8525"]},"institutions":[{"id":"https://openalex.org/I4210096646","display_name":"Gold Coast City Council","ror":"https://ror.org/00d4czm47","country_code":"AU","type":"government","lineage":["https://openalex.org/I4210096646"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hanif Bhuiyan","raw_affiliation_strings":["Performance & Analytics , Gold Coast City Council , Bundall , Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Performance & Analytics , Gold Coast City Council , Bundall , Australia","institution_ids":["https://openalex.org/I4210096646"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.5098000168800354,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.5098000168800354,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.426800012588501,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.010999999940395355,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/probability-levels","display_name":"probability levels","score":0.6880000233650208},{"id":"https://openalex.org/keywords/conditionals","display_name":"conditionals","score":0.5370000004768372},{"id":"https://openalex.org/keywords/forecast-verification","display_name":"forecast verification","score":0.3959999978542328},{"id":"https://openalex.org/keywords/few-shot-learning","display_name":"few-shot learning","score":0.6150000095367432},{"id":"https://openalex.org/keywords/post-editing","display_name":"post-editing","score":0.8949999809265137},{"id":"https://openalex.org/keywords/natural-language-inference","display_name":"natural language inference","score":0.5929999947547913}],"concepts":[{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.7472000122070312},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7258999943733215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6046000123023987},{"id":"https://openalex.org/C7493553","wikidata":"https://www.wikidata.org/wiki/Q1520777","display_name":"Certainty","level":2,"score":0.5702999830245972},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.5616999864578247},{"id":"https://openalex.org/C2780876879","wikidata":"https://www.wikidata.org/wiki/Q3054749","display_name":"Meaning (existential)","level":2,"score":0.4779999852180481},{"id":"https://openalex.org/C2780882242","wikidata":"https://www.wikidata.org/wiki/Q14235582","display_name":"Literal (mathematical logic)","level":2,"score":0.43939998745918274},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4025999903678894},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39809998869895935},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3864000141620636},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.352400004863739},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.34130001068115234},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.3384999930858612},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.325300008058548},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28459998965263367},{"id":"https://openalex.org/C59656382","wikidata":"https://www.wikidata.org/wiki/Q191536","display_name":"Conjunction (astronomy)","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2703000009059906},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26669999957084656},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2662999927997589}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.31223/x5tr4z","doi":"https://doi.org/10.31223/x5tr4z","is_oa":true,"landing_page_url":"https://doi.org/10.31223/x5tr4z","pdf_url":"https://eartharxiv.org/repository/object/15308/download/26528/","source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},{"id":"pmh:oai:EA:id:15308","doi":null,"is_oa":false,"landing_page_url":"https://eartharxiv.org/repository/object/15308/download/26528/","pdf_url":null,"source":{"id":"https://openalex.org/S4306402614","display_name":"EarthArXiv (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EarthArXiv","raw_type":"PREPRINTS"}],"best_oa_location":{"id":"doi:10.31223/x5tr4z","doi":"https://doi.org/10.31223/x5tr4z","is_oa":true,"landing_page_url":"https://doi.org/10.31223/x5tr4z","pdf_url":"https://eartharxiv.org/repository/object/15308/download/26528/","source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7215115154.pdf","grobid_xml":"https://content.openalex.org/works/W7215115154.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W4381827075","https://openalex.org/W4414939355","https://openalex.org/W4415085804","https://openalex.org/W4416931808","https://openalex.org/W7160870532","https://openalex.org/W7167616799","https://openalex.org/W7204454032"],"related_works":["https://openalex.org/W1574486308","https://openalex.org/W2135363470","https://openalex.org/W7100330494","https://openalex.org/W2097187535","https://openalex.org/W7098694491","https://openalex.org/W7098039695","https://openalex.org/W7154944520","https://openalex.org/W7204454032","https://openalex.org/W7140010272","https://openalex.org/W3017041246"],"abstract_inverted_index":{"Weather-to-text":[0],"generation":[1],"must":[2],"preserve":[3],"the":[4,120,130],"event":[5,20,31],"attached":[6],"to":[7,42],"a":[8,67,84,95,138,158],"probability,":[9],"not":[10],"only":[11,151],"its":[12],"numerical":[13],"value.":[14],"We":[15,46],"represent":[16],"rainfall":[17],"statements":[18,105],"through":[19],"thresholds,":[21],"boundary":[22],"inclusion,":[23],"probability":[24,166],"levels":[25],"and":[26,38,55,63,103,170],"conditioning.":[27],"A":[28,144],"verifier":[29],"bounds":[30],"probabilities":[32],"under":[33],"partially":[34],"specified":[35],"exact":[36],"distributions":[37],"separately":[39],"checks":[40],"fidelity":[41],"operational":[43],"reporting":[44],"conventions.":[45],"evaluate":[47],"eight":[48],"model":[49],"systems":[50],"on":[51],"Australian":[52],"forecast":[53],"inputs":[54],"controlled":[56],"transformations,":[57],"with":[58,81,88],"number-copying,":[59],"few-shot,":[60],"post-editing,":[61],"operator-plan":[62],"deterministic-template":[64],"baselines.":[65],"On":[66],"2,304-case":[68],"analytical":[69],"challenge,":[70],"source-blind":[71],"extraction":[72,171],"followed":[73],"by":[74],"verification":[75],"gives":[76],"91.36%":[77],"correct":[78],"labels,":[79],"compared":[80],"30.43%":[82],"for":[83],"text-only":[85],"NLI":[86],"baseline":[87],"different":[89],"information":[90],"access.":[91],"Generation":[92],"results":[93,163],"expose":[94],"measurement":[96],"problem:":[97],"definitions":[98],"can":[99,106,125],"become":[100],"certainty":[101,155],"assertions,":[102],"unconditional":[104],"acquire":[107],"conditions":[108],"during":[109],"extraction.":[110],"Exact":[111],"output":[112],"comparisons":[113],"also":[114],"show":[115],"that":[116,150],"planning":[117],"often":[118],"reproduces":[119],"deterministic":[121],"template,":[122],"while":[123],"post-editing":[124],"leave":[126],"drafts":[127],"unchanged.":[128],"Under":[129],"residual-corrected":[131],"estimator,":[132],"1,000":[133],"sampled":[134],"analysis":[135],"units":[136],"retain":[137],"pooled":[139],"few-shot":[140],"advantage":[141],"in":[142],"preservation.":[143],"separate":[145],"223-target":[146],"human":[147],"study":[148],"estimates":[149],"1.32--5.80%":[152],"of":[153],"extracted":[154],"assertions":[156],"match":[157],"definite":[159],"literal":[160],"reading.":[161],"These":[162],"support":[164],"evaluating":[165],"operators,":[167],"source":[168],"coverage":[169],"validity":[172],"together.":[173]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-10-02T00:00:00"}
