{"id":"W7083581858","doi":"10.22161/ijels.105.32","title":"Optimizing the Production of Proficient Explosive Detection Dogs: An Analysis of the Criteria in Selecting Puppies for Training","year":2025,"lang":"en","type":"article","venue":"International Journal of English Literature and Social Sciences","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Quality (philosophy); Production (economics); Government (linguistics); Training (meteorology); Procurement; Puppy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004474449,0.0002019534,0.0001792471,0.0006570654,0.0006209242,0.0008951562,0.0004710794,0.0002660118,0.001866594],"category_scores_gemma":[0.01329636,0.0001251075,0.0001627195,0.0005067905,0.0003709787,0.0005699462,0.0008376445,0.0002599824,0.0002312027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001009271,"about_ca_system_score_gemma":0.001638002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001791938,"about_ca_topic_score_gemma":0.006689716,"domain_scores_codex":[0.997364,0.001335047,0.0001837774,0.0002065457,0.00061621,0.0002943408],"domain_scores_gemma":[0.9925302,0.003014103,0.001831432,0.000141458,0.001454908,0.001027842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007336752,0.0007426093,0.7918394,0.0007078496,0.00007684591,0.0007736044,0.01678833,0.0006602743,0.01008355,0.0007948403,0.0016131,0.1751859],"study_design_scores_gemma":[0.00003181722,0.001745224,0.9365392,0.0003614302,0.00009046395,0.000906145,0.04450306,0.002633124,0.004389873,0.0003709811,0.008377544,0.00005118584],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974148,0.000100841,0.0008553955,0.00008578839,0.000005506395,0.00006808728,0.00003495936,0.000004834854,0.0014298],"genre_scores_gemma":[0.9947816,0.0001550251,0.004309661,0.00003394957,0.000002893156,0.00004928678,0.00005360172,0.00000479512,0.0006091977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004474449,"threshold_uncertainty_score":0.0236634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01882087965089828,"score_gpt":0.312550212313194,"score_spread":0.2937293326622957,"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."}}