{"id":"W4393009583","doi":"10.2139/ssrn.4765840","title":"Leveraging Crowdsourced Data for Extreme Heat Monitoring","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Crowdsourcing; Extreme heat; Computer science; Extreme environment; Data science; Climate change; World Wide Web; Geology; Biology; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001783677,0.001141263,0.001023662,0.002347917,0.0005809455,0.001583646,0.001472205,0.001638177,0.003122214],"category_scores_gemma":[0.01128043,0.0004607598,0.0007477482,0.002989865,0.0004305046,0.002132511,0.002971703,0.001252598,0.00239741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003875179,"about_ca_system_score_gemma":0.0007715417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007610381,"about_ca_topic_score_gemma":0.01279164,"domain_scores_codex":[0.9984148,0.0005094913,0.00007320275,0.0004171284,0.0004723273,0.000113094],"domain_scores_gemma":[0.9953279,0.00232746,0.0002258006,0.001329935,0.0006356331,0.0001532339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001546607,0.001135815,0.03692722,0.001677081,0.001436295,0.0008584821,0.001118664,0.3081512,0.02930347,0.007434441,0.103144,0.5072666],"study_design_scores_gemma":[0.0001150799,0.0001587387,0.0132266,0.0001460248,0.0001837188,0.0001131248,0.0007844597,0.9009346,0.009929987,0.03968374,0.0346196,0.0001041999],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2958078,0.00522628,0.5549977,0.005939866,0.00580009,0.001283529,0.06488033,0.01771357,0.04835078],"genre_scores_gemma":[0.850588,0.0007122068,0.1090971,0.0006285401,0.001075676,0.0004077755,0.03095021,0.0006208643,0.005919721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007610381,"threshold_uncertainty_score":0.01513219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05549339051782928,"score_gpt":0.2787883334474002,"score_spread":0.2232949429295709,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}