{"id":"W2015136852","doi":"10.1007/s00704-013-0839-7","title":"Selecting the best performing fire weather indices for Austrian ecoregions","year":2013,"lang":"en","type":"article","venue":"Theoretical and Applied Climatology","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund; Institut National Du Cancer; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Austrian Science Fund","keywords":"Environmental science; Index (typography); Percentile; Meteorology; Ecoregion; Terrain; Climatology; Geography; Statistics; Computer science; Mathematics; Cartography; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001364932,0.0007353878,0.0007222821,0.001320496,0.0002458868,0.001065933,0.0004102687,0.0003790113,0.0005011272],"category_scores_gemma":[0.002520575,0.0002108306,0.0006633505,0.0008587934,0.0001642857,0.0005764299,0.0005521686,0.0003105613,0.0003064452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004197113,"about_ca_system_score_gemma":0.0004133685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005190001,"about_ca_topic_score_gemma":0.005255448,"domain_scores_codex":[0.9996854,0.0001010935,0.00002561373,0.00006665019,0.00005138465,0.00006990098],"domain_scores_gemma":[0.999339,0.0002491156,0.0001435318,0.00006581948,0.0001386002,0.00006391097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000492672,0.0002161129,0.500254,0.0001323075,0.0003142136,0.0002131035,0.0002294832,0.2962231,0.01537861,0.001146088,0.001590717,0.1838096],"study_design_scores_gemma":[0.00003235569,0.0002771333,0.2823145,0.00008750687,0.0001585905,0.0001215061,0.0004128485,0.7021459,0.0104796,0.001826728,0.002085639,0.00005760071],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9442452,0.0004666448,0.05261633,0.00008017854,0.00002180178,0.00003321801,0.0004480875,0.0003059488,0.001782582],"genre_scores_gemma":[0.9625786,0.0001414065,0.0360294,0.00001229783,0.000009061842,0.00002385067,0.0007640438,0.00004099831,0.0004002213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005190001,"threshold_uncertainty_score":0.01031959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006657987991753614,"score_gpt":0.2159021810422137,"score_spread":0.2092441930504601,"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."}}