{"id":"W4301398046","doi":"10.20944/preprints202210.0012.v1","title":"A Method for Improving Interspecies Welfare Comparisons","year":2022,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Human-Animal Interaction Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Victoria","funders":"Open Philanthropy Project","keywords":"Welfare; Animal welfare; Public economics; Operationalization; Harm; Construct (python library); Economics; Psychology; Social psychology; Ecology; Biology; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006230185,0.0004146167,0.0005083344,0.0001199461,0.0003794315,0.00004061926,0.0007741468,0.000267281,0.00169074],"category_scores_gemma":[0.0004550294,0.0004601785,0.0005111509,0.00005649752,0.00008919206,0.000004728964,0.006734382,0.0007423188,0.00007846462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001519815,"about_ca_system_score_gemma":0.00009819376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002278369,"about_ca_topic_score_gemma":0.0001494318,"domain_scores_codex":[0.9973811,0.0001964639,0.0005270059,0.001301383,0.0002134785,0.0003805883],"domain_scores_gemma":[0.9980487,0.00005765426,0.0004249417,0.001104196,0.0002747735,0.00008973672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001101389,0.0005663941,0.09799299,0.0009425314,0.00233129,0.00001117997,0.002462351,0.001793633,0.8746414,0.001859923,0.01414388,0.002153003],"study_design_scores_gemma":[0.0004807611,0.0001476151,0.02783761,0.00003303089,0.0001715358,0.00001284264,0.003725848,0.0003143499,0.1503799,0.0001328833,0.8160785,0.0006851158],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9161579,0.001367387,0.04776078,0.003219533,0.003029312,0.002499791,0.0006577669,0.0002427426,0.02506481],"genre_scores_gemma":[0.9801379,0.00006512798,0.009985254,0.0002557886,0.0004775003,0.001397838,0.0003733191,0.00008046076,0.007226794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8019347,"threshold_uncertainty_score":0.999785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1470985192699573,"score_gpt":0.4529299394452401,"score_spread":0.3058314201752829,"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."}}