{"id":"W2612286416","doi":"","title":"The Effect of Month and Season Upon Breeding Efficiency Obtained with Artificial Insemination Under Ontario Conditions","year":2016,"lang":"en","type":"article","venue":"Canadian journal of agricultural science/Canadian Journal of Agricultural Science","topic":"Plant Physiology and Cultivation Studies","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial insemination; Spring (device); Significant difference; Insemination; Winter season; Seasonal breeder; Biology; Demography; Animal science; Mathematics; Ecology; Pregnancy; Climatology; Engineering; Botany; Statistics; Sperm","routes":{"ca_aff":false,"ca_fund":false,"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":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.002197809,0.0002848021,0.0004347376,0.0002132436,0.003290762,0.0004258016,0.001189574,0.00009562206,0.00006085117],"category_scores_gemma":[0.000705253,0.00007034024,0.000156085,0.002376711,0.003141955,0.001965967,0.00003574893,0.0003396359,0.000002895273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008535882,"about_ca_system_score_gemma":0.001954129,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02850752,"about_ca_topic_score_gemma":0.7056193,"domain_scores_codex":[0.9970375,0.00011775,0.00073476,0.0003050436,0.0009003087,0.0009046211],"domain_scores_gemma":[0.9946693,0.0005410562,0.001070865,0.00006812513,0.002071229,0.001579465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00006386876,0.00001488235,0.06019373,0.000004539379,0.00004960139,0.00002530005,0.001111783,0.000177483,0.9282866,0.001870365,0.0008251352,0.007376748],"study_design_scores_gemma":[0.0003010297,0.001895805,0.9768263,0.0002376577,0.00005199093,0.0008146128,0.003268607,0.000001655737,0.015718,0.0001798799,0.0004813205,0.0002231275],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935629,0.0002668593,0.000002946575,0.005017655,0.0005473888,0.0002112614,0.00003561214,0.000004279397,0.0003510536],"genre_scores_gemma":[0.9994914,0.00002769572,0.00003177755,0.00003906223,0.0002533043,0.000002372155,0.000002807642,0.000001124056,0.0001504837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9166326,"threshold_uncertainty_score":0.9995709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01028484447069333,"score_gpt":0.1947055520591098,"score_spread":0.1844207075884165,"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."}}