{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002382576,0.0001839712,0.0005864838,0.0003119073,0.00007077252,0.00001305151,0.0002586739,0.00008367909,0.0004549899],"category_scores_gemma":[0.00004730173,0.0002169391,0.0002118814,0.0008464214,0.0001768912,0.0004067175,0.00007240314,0.0005050879,0.00001005432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001963501,"about_ca_system_score_gemma":0.0001739124,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00870345,"about_ca_topic_score_gemma":0.001431173,"domain_scores_codex":[0.998713,0.00008303525,0.0002435256,0.0003211325,0.0003189533,0.0003203726],"domain_scores_gemma":[0.998911,0.00006544658,0.0002758068,0.0004359981,0.00006665708,0.0002451013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002434469,0.0006143652,0.5675097,0.00008953936,0.0002115576,0.0005593567,0.001413473,0.00008830673,0.007232539,0.0001582828,0.04333125,0.3763572],"study_design_scores_gemma":[0.003263824,0.001655325,0.9683514,0.0004837211,0.0001956905,0.00003823091,0.007105828,0.0002704999,0.0003116152,0.0002043366,0.01775611,0.0003634572],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980513,0.0003699315,0.0007435667,0.01218208,0.00007617027,0.0002363245,0.00001662607,0.00004420672,0.005818131],"genre_scores_gemma":[0.9899752,0.0002541155,0.007602761,0.0009206933,0.00004829491,1.213874e-7,0.0000248526,0.00001446147,0.001159542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4008417,"threshold_uncertainty_score":0.9978977,"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."}}