{"id":"W4408197200","doi":"10.1101/2025.03.05.641645","title":"Remote inferences and direct observations provide complementary insights into foraging behavior","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Animal Vocal Communication and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Foraging; Computer science; Data science; Biology; Ecology","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":[0.006436474,0.0007263117,0.0008109749,0.005479867,0.0006029871,0.003230373,0.001072408,0.000907082,0.008255428],"category_scores_gemma":[0.02022602,0.0006333512,0.0008852645,0.002226561,0.002682816,0.005703106,0.003528347,0.0009443599,0.001028035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005881369,"about_ca_system_score_gemma":0.0004474082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002312471,"about_ca_topic_score_gemma":0.006792889,"domain_scores_codex":[0.9929373,0.003539903,0.0006139503,0.001195876,0.001472699,0.0002403193],"domain_scores_gemma":[0.9682709,0.02008686,0.006213455,0.003089498,0.00189826,0.0004410714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009037691,0.0003038533,0.5453254,0.006669996,0.001837277,0.0008042398,0.01406237,0.004160802,0.02454812,0.02129502,0.001738909,0.3783502],"study_design_scores_gemma":[0.00004974915,0.0004411135,0.909887,0.00204901,0.0008313513,0.0009217252,0.01306305,0.008936825,0.005560828,0.04035548,0.01773484,0.0001690184],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7887434,0.01532533,0.1047288,0.001341415,0.0001360757,0.0002338331,0.001986904,0.0003199337,0.08718437],"genre_scores_gemma":[0.9689734,0.002107553,0.02619668,0.0002565099,0.00009744128,0.00008321711,0.0004145508,0.00004960177,0.001821053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008255428,"threshold_uncertainty_score":0.03403968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03772061673197899,"score_gpt":0.2799405148925225,"score_spread":0.2422198981605435,"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."}}