{"id":"W6969612503","doi":"10.5683/sp3/dyene2","title":"Sierra Crest EDS Point Spectra Data","year":2023,"lang":"en","type":"dataset","venue":"Borealis","topic":"Big Data and Digital Economy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Crest; Point (geometry); Spectral line; Identification (biology)","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":[0.0004839828,0.001761526,0.0009785682,0.003237462,0.0008590029,0.001341889,0.00198399,0.00136565,0.01906308],"category_scores_gemma":[0.001571331,0.0005111514,0.001041709,0.004558811,0.0004594222,0.0006857171,0.001119904,0.001089652,0.02503783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009384021,"about_ca_system_score_gemma":0.001875358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06883515,"about_ca_topic_score_gemma":0.1359062,"domain_scores_codex":[0.9993767,0.0000394793,0.00004114981,0.0001603943,0.0002719062,0.0001105034],"domain_scores_gemma":[0.9990826,0.0001113023,0.00007299175,0.0001854855,0.0004343998,0.0001132543],"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":[0.0001481362,0.0001309594,0.004410488,0.0006600631,0.00007111525,0.000126636,0.00009600866,0.001423761,0.00178478,0.000656358,0.9821773,0.008314459],"study_design_scores_gemma":[0.0002345544,0.00003419866,0.03140144,0.0001442317,0.00005599225,0.0001329175,0.000248377,0.00166901,0.001967067,0.000942675,0.9631071,0.00006239075],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001517713,0.00006109016,0.0001202409,0.00004222465,0.00002655041,0.0000177395,0.9962977,0.0007266965,0.001190153],"genre_scores_gemma":[0.001738531,0.00003910955,0.0004806235,0.00002110007,0.000005920921,0.00003600873,0.9970093,0.00007256094,0.0005968674],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06883515,"threshold_uncertainty_score":0.136869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07064397719674667,"score_gpt":0.2883944461778532,"score_spread":0.2177504689811065,"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."}}