{"id":"W4402161815","doi":"10.1002/ecs2.4956","title":"Extending Canadian forest disturbance history maps prior to 1985","year":2024,"lang":"en","type":"article","venue":"Ecosphere","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Canadian Forest Service","keywords":"Disturbance (geology); Forest inventory; Mountain pine beetle; Environmental science; Mean squared error; Forest management; Geography; Physical geography; Forestry; Agroforestry; Statistics; Mathematics; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0004263466,0.0003851374,0.0001650104,0.002295613,0.0007426986,0.0008158533,0.0004343518,0.000146648,0.001064457],"category_scores_gemma":[0.001423069,0.0001248276,0.0002460714,0.002542556,0.000196585,0.0002752206,0.0004213025,0.0002276149,0.0002064796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008950153,"about_ca_system_score_gemma":0.005385145,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9728897,"about_ca_topic_score_gemma":0.9886741,"domain_scores_codex":[0.999731,0.00001197828,0.00001422758,0.00006708662,0.00009930024,0.00007646116],"domain_scores_gemma":[0.9988942,0.00009058555,0.0001405166,0.00005329833,0.0007501735,0.00007132853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001240342,0.00004557711,0.9065008,0.00008695926,0.00007043418,0.0001443974,0.0006801495,0.0185257,0.002179322,0.0003757251,0.005250188,0.06601667],"study_design_scores_gemma":[0.000002702926,0.000006592725,0.9853501,0.00002678687,0.00001297997,0.00001743529,0.0002701444,0.00985931,0.0007158933,0.00004358089,0.003683397,0.00001104139],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9661053,0.0003428271,0.003927789,0.00007763805,0.00001253845,0.00007677827,0.02094027,0.000241189,0.008275724],"genre_scores_gemma":[0.9836534,0.0001927752,0.002896876,0.0000202232,0.000003751584,0.00002438351,0.01198354,0.00001455197,0.001210593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02711034,"threshold_uncertainty_score":0.06493825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005566547706981484,"score_gpt":0.1886195293338169,"score_spread":0.1830529816268354,"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."}}