{"id":"W6959064537","doi":"10.7281/t1/bmathh/fhrjxw","title":"22_D2.7z.049","year":2021,"lang":"en","type":"dataset","venue":"Research Data Repository, Duke University","topic":"Sesame and Sesamin Research","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Process (computing); Compression (physics); Object (grammar)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001204,0.002616963,0.001584672,0.00397629,0.001124667,0.002983784,0.003408887,0.002262531,0.2259144],"category_scores_gemma":[0.006569438,0.000871496,0.001727008,0.006295448,0.0005770425,0.001495521,0.002461473,0.001805091,0.2674947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001624678,"about_ca_system_score_gemma":0.002763211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02578148,"about_ca_topic_score_gemma":0.03716585,"domain_scores_codex":[0.9986923,0.0002125165,0.0001567041,0.000406933,0.0002763904,0.0002551466],"domain_scores_gemma":[0.9972461,0.0007115814,0.0001994076,0.0007315788,0.0007692033,0.0003421455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000599848,0.00001855319,0.0002761401,0.0002874184,0.00001594361,0.00001138446,0.00001116872,0.000125505,0.0001193199,0.0002151564,0.997965,0.0008944453],"study_design_scores_gemma":[0.0004617276,0.00003554358,0.00342875,0.0001866139,0.00003736602,0.00005918969,0.00009512527,0.0004972858,0.0007541829,0.001288337,0.9931164,0.00003946762],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006467135,0.0000174123,0.00003897257,0.00004110757,0.00002040737,0.000009028999,0.9989852,0.0003649152,0.0004583313],"genre_scores_gemma":[0.0002312794,0.00002059738,0.0001423569,0.00003458645,0.000008378379,0.00004814954,0.9986958,0.000130289,0.0006886398],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7740856,"threshold_uncertainty_score":0.7557591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2497794765101772,"score_gpt":0.3580022120208136,"score_spread":0.1082227355106364,"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."}}