{"id":"W7126189096","doi":"10.14288/1.0451392","title":"Testing the use of remote cameras to index body condition in brown and black bears","year":2025,"lang":"","type":"dataset","venue":"Open Collections","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Population; Condition index; Ursus; Metric (unit); Repeatability; Physiological condition; Measure (data warehouse)","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":[0.001904085,0.0004837788,0.0002480983,0.0004981513,0.0004163367,0.0004703325,0.0005022242,0.000497672,0.0008239963],"category_scores_gemma":[0.002355661,0.0002077888,0.0004249148,0.0002335962,0.0003529492,0.000733799,0.0003992974,0.0003704653,0.0002700792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004714376,"about_ca_system_score_gemma":0.0002982976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01424944,"about_ca_topic_score_gemma":0.03544742,"domain_scores_codex":[0.9991853,0.0002456724,0.00003845125,0.0002969097,0.000171283,0.00006240658],"domain_scores_gemma":[0.9978312,0.000524301,0.000736104,0.0001591413,0.0005491966,0.000200086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002140932,0.0002428186,0.9893658,0.0000211003,0.000103996,0.00002937574,0.0001799328,0.0008312311,0.003702592,0.00003012093,0.0001209511,0.005157964],"study_design_scores_gemma":[0.00001050293,0.0006921348,0.9934264,0.00001081989,0.0000448247,0.000064331,0.0002251849,0.003737838,0.001628223,0.00002268351,0.0001294624,0.000007693544],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9988512,0.0000362797,0.0005353321,0.00001548668,0.00000693883,0.00002239004,0.000166592,0.00001185348,0.0003540446],"genre_scores_gemma":[0.9979305,0.00002925,0.001500759,0.00003298127,0.000009423193,0.00004520998,0.0002740599,0.000003985163,0.000173798],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.01424944,"threshold_uncertainty_score":0.02833301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06070304847820347,"score_gpt":0.3226274202868099,"score_spread":0.2619243718086064,"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."}}