{"id":"W4412148042","doi":"10.20944/preprints202507.0728.v1","title":"Dairy DigiD: An Edge-Cloud Framework for Real-Time Cattle Biometrics and Health Classification","year":2025,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Food Supply Chain Traceability","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Biometrics; Cloud computing; Enhanced Data Rates for GSM Evolution; Business; Computer science; Computer security; Artificial intelligence; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001899149,0.0003894858,0.0005979759,0.00009086796,0.0003360495,0.00008658673,0.0008432637,0.0007142033,0.0003316264],"category_scores_gemma":[0.0008389195,0.0002068442,0.0001901279,0.0007188274,0.0001765434,0.0001133392,0.001082086,0.0006342276,0.00008980559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002694424,"about_ca_system_score_gemma":0.0001444293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001314692,"about_ca_topic_score_gemma":0.0001948319,"domain_scores_codex":[0.9966066,0.0002754024,0.0006659367,0.001629504,0.0003149996,0.0005075929],"domain_scores_gemma":[0.9974758,0.0008813104,0.0004329032,0.0006590831,0.0002312512,0.0003196542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000184342,0.001356173,0.850938,0.001138714,0.0001458436,9.615017e-7,0.001358318,0.00004466964,0.02413257,0.005556711,0.001145307,0.1139984],"study_design_scores_gemma":[0.0001168643,0.000169514,0.9700161,0.0001428737,0.00002833404,0.000001059241,0.0002484522,0.0002718154,0.00244958,0.01765998,0.008476714,0.0004186904],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838279,0.0002364934,0.0002016172,0.01075081,0.0007744708,0.00206481,0.0008414647,0.0003554105,0.0009470494],"genre_scores_gemma":[0.9932511,0.0006832684,0.002652054,0.0002624294,0.0006070152,0.0003822996,0.001216373,0.000004949269,0.0009405538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1190782,"threshold_uncertainty_score":0.8434861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2134599623673585,"score_gpt":0.3841494018129865,"score_spread":0.1706894394456279,"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."}}