{"id":"W4237786825","doi":"10.1515/iupac.79.1599","title":"Meiosis","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Biomedical Research and Pathophysiology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Toxicology; Chemistry; Biology; Philosophy; Linguistics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005198885,0.0003421096,0.0008605233,0.0002831396,0.00006650996,0.00001444378,0.0002931814,0.0007066315,0.01598811],"category_scores_gemma":[0.002589375,0.0002054984,0.0002625249,0.0002297424,0.0004340299,0.00002835904,0.0001963378,0.0008772839,0.00002629601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003077845,"about_ca_system_score_gemma":0.001957017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007893441,"about_ca_topic_score_gemma":0.00009169282,"domain_scores_codex":[0.9967585,0.0001070991,0.0003883351,0.0005181644,0.001572248,0.0006557131],"domain_scores_gemma":[0.9975374,0.0002051669,0.0001171456,0.0008903691,0.0005125826,0.0007373905],"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":[0.0007904391,0.000295084,0.000005158611,0.0003756996,0.0001690408,0.0004668715,0.000001671453,2.94229e-9,0.0002446623,0.000003312752,0.9760991,0.02154891],"study_design_scores_gemma":[0.001739125,0.001315826,0.0001209188,0.0007232488,0.0001452732,0.00005579177,0.000003952175,9.406945e-7,0.00004918452,0.0001851157,0.9954411,0.0002195647],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007959392,0.001096876,0.00005126996,0.005192277,0.0004637525,0.0003318452,0.9926211,0.00005316651,0.0001100996],"genre_scores_gemma":[0.0000155734,0.005103925,0.00005678208,0.001838716,0.002168762,0.0000203421,0.9895514,0.0000319807,0.001212564],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02132934,"threshold_uncertainty_score":0.9849114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02435102930033458,"score_gpt":0.4681255523724061,"score_spread":0.4437745230720715,"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."}}