{"id":"W4242713575","doi":"10.32920/14638737","title":"Mapping Burn Severity of Forest Fires in Small Sample Size Scenarios.","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; National Park Service; U.S. Forest Service; U.S. Geological Survey; National Natural Science Foundation of China; Chengdu University of Information Technology; U.S. Department of Agriculture; Central South University; National Science Foundation","keywords":"Regression; Regression analysis; Sample size determination; Sample (material); Mean squared error; Generalization; Statistics; Linear regression; Computer science; Environmental science; Mathematics","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":[0.002602964,0.0006381953,0.0003947615,0.0009362212,0.0002130224,0.0002862983,0.0004247621,0.0003431176,0.0009508021],"category_scores_gemma":[0.006658885,0.0001711529,0.000467833,0.0006906218,0.0001739659,0.000665091,0.000288141,0.0004495716,0.0001995354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000173413,"about_ca_system_score_gemma":0.000197461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002711596,"about_ca_topic_score_gemma":0.006271147,"domain_scores_codex":[0.9994878,0.0002531733,0.00003242856,0.0001247802,0.00007374835,0.00002807299],"domain_scores_gemma":[0.9978918,0.001245908,0.0003030924,0.0002999194,0.0001726051,0.0000865928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009891491,0.0006100248,0.4026975,0.0005395842,0.0007435575,0.0005603745,0.0004351555,0.2679349,0.02666578,0.001273562,0.002701374,0.294849],"study_design_scores_gemma":[0.00004196265,0.0003173223,0.2991315,0.00004596221,0.0001330647,0.0003064038,0.0003785172,0.685977,0.008495091,0.003700856,0.001432511,0.00003971107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8475359,0.0003900948,0.1484474,0.000115544,0.00004401807,0.0001272496,0.001155755,0.00052118,0.001662728],"genre_scores_gemma":[0.9625425,0.0001010763,0.03544168,0.00002911932,0.00001238696,0.00006361646,0.001417139,0.00002602688,0.0003665163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002711596,"threshold_uncertainty_score":0.01376593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01487004481692289,"score_gpt":0.2145950491962098,"score_spread":0.1997250043792869,"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."}}