{"id":"W6908118164","doi":"10.25549/whit-c170-113041","title":"Mountains along Highway 395 (winter), USA, 1946","year":2021,"lang":"en","type":"dataset","venue":"University of Southern California Digital Library","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vegetation (pathology); Highway system; Shore; Hydrology (agriculture)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00009792412,0.0008515277,0.001078017,0.0006313995,0.0002443908,0.0004467454,0.002002279,0.0007168892,0.01430068],"category_scores_gemma":[0.00006615986,0.0009703587,0.0008211038,0.0007061171,0.0007458851,0.001125511,0.002127007,0.0008614087,0.2936836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000212829,"about_ca_system_score_gemma":0.0007172474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004645025,"about_ca_topic_score_gemma":0.0001900653,"domain_scores_codex":[0.9965668,0.0001524584,0.0005632257,0.001072343,0.0008941285,0.0007510225],"domain_scores_gemma":[0.9970518,0.000173489,0.000716935,0.001421194,0.0001213459,0.000515176],"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":[0.0002826607,0.000399997,0.004060959,0.000278964,0.0004118044,0.001451258,0.00005717902,0.00001282897,0.00001077883,0.000002588867,0.9928778,0.0001531339],"study_design_scores_gemma":[0.0008120776,0.00005356431,0.00003558492,0.0004348366,0.0002495175,0.00004247848,0.00241336,0.000009964607,0.00003986479,0.00007394856,0.9948884,0.0009464343],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00472948,0.0002111166,0.00001503376,0.0001169881,0.0001271151,0.0002939339,0.9911848,0.0003032954,0.003018196],"genre_scores_gemma":[0.0009842118,0.0000544271,0.0001172355,0.0001393398,0.0002260049,3.875722e-7,0.9940147,0.0002054068,0.004258241],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2793829,"threshold_uncertainty_score":0.9992747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008169858776196188,"score_gpt":0.1713849540252146,"score_spread":0.1632150952490184,"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."}}