{"id":"W4235010609","doi":"10.1515/iupac.85.0459","title":"First Stability Region","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; National Research Council Canada","funders":"","keywords":"Chemical nomenclature; Terminology; Mass spectrometry; Chemistry; Standardization; Tandem mass spectrometry; Accelerator mass spectrometry; Analytical Chemistry (journal); Computer science; Chromatography; Organic chemistry; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001983982,0.002124028,0.00184083,0.006346729,0.001608174,0.006433964,0.003118749,0.002049268,0.243314],"category_scores_gemma":[0.01984403,0.0007561586,0.002243648,0.01067736,0.0005450202,0.004373115,0.002923275,0.002261612,0.3694094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002847711,"about_ca_system_score_gemma":0.00507448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02478748,"about_ca_topic_score_gemma":0.02936694,"domain_scores_codex":[0.9965103,0.0004465636,0.000578602,0.001086952,0.0009693688,0.0004082163],"domain_scores_gemma":[0.9888586,0.002309091,0.00110543,0.002529126,0.00473615,0.0004617746],"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":[0.00006641843,0.000007582617,0.0006009314,0.0006782655,0.00002087411,0.00001385421,0.00002315294,0.00009056905,0.00007668484,0.0008722733,0.9920218,0.005527388],"study_design_scores_gemma":[0.00003734648,0.000006195832,0.001677943,0.0003852912,0.00001621914,0.00003114021,0.00005463901,0.00009274849,0.0001786218,0.001228782,0.996274,0.00001710349],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006514894,0.0001505481,0.0001648764,0.0001283671,0.00005829219,0.00001648768,0.9962834,0.0005061969,0.002626856],"genre_scores_gemma":[0.0003750632,0.0002401829,0.0004708016,0.0001307457,0.00002666183,0.0000977495,0.9957733,0.0002696162,0.002615903],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.243314,"threshold_uncertainty_score":0.8139666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01963692691883311,"score_gpt":0.3734133294936962,"score_spread":0.353776402574863,"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."}}