{"id":"W4396886518","doi":"10.2139/ssrn.4822314","title":"A Deep Learning Approach to Predict Fearfulness in Laying Hen Pullets","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hendrix Genetics (Canada)","funders":"","keywords":"Laying; Artificial intelligence; Biology; Computer science; Animal science; Engineering; Structural engineering","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":[0.0003654219,0.0005648204,0.0003305537,0.0003563681,0.0001823499,0.000331128,0.0004742631,0.0006312131,0.001154976],"category_scores_gemma":[0.0007343797,0.0001757675,0.0003917287,0.0002280396,0.000167882,0.0002907884,0.0004431044,0.0007326803,0.0002368368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003650874,"about_ca_system_score_gemma":0.0004048918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00603785,"about_ca_topic_score_gemma":0.006700464,"domain_scores_codex":[0.999912,0.00001577545,0.000003724797,0.00002892506,0.00001130605,0.00002819628],"domain_scores_gemma":[0.9996024,0.0002280666,0.00003254811,0.00002214649,0.00008030153,0.00003455475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0009379284,0.001331527,0.04125081,0.0001009029,0.0003058962,0.0001538198,0.00007908071,0.5518719,0.02366504,0.001403127,0.003839745,0.3750602],"study_design_scores_gemma":[0.000004791445,0.00008022887,0.003700804,0.00000340914,0.00001315726,0.000007012467,0.000008658943,0.9945881,0.001033124,0.0004933532,0.00006310559,0.000004140114],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7741739,0.0005329649,0.2213972,0.0004081842,0.0001467306,0.00005349897,0.0005689678,0.0006951336,0.002023453],"genre_scores_gemma":[0.9831131,0.00008777465,0.01386519,0.00006014717,0.00003581359,0.00003408308,0.0003985114,0.00001426665,0.00239106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00603785,"threshold_uncertainty_score":0.01200545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03346652201629005,"score_gpt":0.3102412937579588,"score_spread":0.2767747717416688,"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."}}