{"id":"W7033651835","doi":"","title":"Records of Recovering American Marten, Martes americana, in New Hampshire","year":2009,"lang":"en","type":"article","venue":"Scholarworks (University of Massachusetts Amherst)","topic":"Legal Cases and Commentary","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Marten; Range (aeronautics); Snow; White (mutation); Historical record; Abundance (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":[0.0004654597,0.00007160272,0.0001114248,0.00168697,0.001300469,0.0005600508,0.0006757914,0.0004804646,0.001811541],"category_scores_gemma":[0.002904429,0.0001336528,0.00005094202,0.002082484,0.0006978918,0.000340851,0.0005445228,0.0004762789,0.0002649919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003965211,"about_ca_system_score_gemma":0.002272806,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.688059,"about_ca_topic_score_gemma":0.9106538,"domain_scores_codex":[0.999726,0.00003527736,0.00003413692,0.00004328592,0.0001165205,0.00004482073],"domain_scores_gemma":[0.9976538,0.0006964373,0.0007101852,0.00007464025,0.0007155047,0.0001495537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000167764,0.00006292289,0.674049,0.000851183,0.00005057317,0.01092487,0.04871997,0.0003521884,0.002499126,0.001517122,0.1454185,0.1153868],"study_design_scores_gemma":[0.000006206375,0.00002535878,0.9122892,0.0003244462,0.00002652387,0.001236169,0.0179955,0.0001624221,0.000310398,0.00009108355,0.06751192,0.00002077903],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9463957,0.007843763,0.0001275451,0.01057699,0.0002710524,0.00004185187,0.006207813,0.00002282793,0.02851242],"genre_scores_gemma":[0.9688256,0.01103601,0.0002687353,0.002677674,0.0002901772,0.00003648365,0.002284971,0.00001005787,0.01457041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.688059,"threshold_uncertainty_score":0.6275562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138939462949411,"score_gpt":0.2289527202566129,"score_spread":0.2175633256271187,"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."}}