{"id":"W4393063361","doi":"10.3819/ccbr.2024.190016","title":"Taking Welfare into Account in Comparative Cognition Research","year":2024,"lang":"en","type":"article","venue":"Comparative Cognition & Behavior Reviews","topic":"Qualitative Comparative Analysis Research","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Comparative cognition; Psychology; Comparative psychology; Cognition; Comparative research; Animal behavior; Cognitive psychology; Animal cognition; Welfare; Animal welfare; Cognitive science; Neuroscience; Ecology; Economics; Zoology; Sociology; Biology; Social science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.01069877,0.0005114805,0.001378279,0.001727727,0.001847663,0.0009749299,0.0007556919,0.0002211247,0.008952658],"category_scores_gemma":[0.001156761,0.0004781997,0.0004083621,0.00603639,0.002030679,0.001589074,0.0002254401,0.001766673,0.008252367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001110883,"about_ca_system_score_gemma":0.0005237892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001148302,"about_ca_topic_score_gemma":0.01207505,"domain_scores_codex":[0.9821581,0.01200422,0.001433062,0.001260199,0.002035691,0.001108774],"domain_scores_gemma":[0.9943255,0.001946982,0.0003566575,0.0004336181,0.002562007,0.0003752795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004187479,0.003343146,0.003682096,0.001360697,0.0004797369,0.0004506233,0.5866418,0.00003208025,0.01898792,0.1955927,0.07633885,0.1126716],"study_design_scores_gemma":[0.0007691144,0.0002854123,0.01278734,0.002497495,0.0002770148,0.00000511903,0.08150831,0.0005234763,0.002211413,0.008815142,0.8893304,0.0009898038],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4810074,0.1114032,0.004085327,0.005178663,0.001144874,0.01519528,0.0002237685,0.0006155699,0.3811459],"genre_scores_gemma":[0.9888766,0.004652275,0.0004873885,0.0001069505,0.0003396569,0.003742449,0.0003684944,0.0000344371,0.001391755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8129915,"threshold_uncertainty_score":0.9997669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6419480512249983,"score_gpt":0.6275588080872417,"score_spread":0.01438924313775658,"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."}}