{"id":"W6954550565","doi":"10.57830/2301763","title":"Upper Yukon peregrine falcon dataset","year":2023,"lang":"en","type":"dataset","venue":"National Park Service","topic":"Bone Tissue Engineering Materials","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Falcon; Wildlife refuge; Data collection; Range (aeronautics)","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":[],"category_scores_codex":[0.0003159523,0.0004422768,0.0004032853,0.0003019526,0.00005126018,0.00009818919,0.0006029616,0.000402326,0.0006467894],"category_scores_gemma":[0.00009406251,0.0004965629,0.00005342867,0.0004510058,0.00001184891,0.0001700466,0.0001612331,0.0004317242,0.02679302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001954701,"about_ca_system_score_gemma":0.0000577223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004465022,"about_ca_topic_score_gemma":0.0003886166,"domain_scores_codex":[0.9980758,0.00002930361,0.0004224429,0.0003674418,0.0007381363,0.0003668168],"domain_scores_gemma":[0.9989709,0.0001603631,0.00006527746,0.0005357309,0.0001567782,0.0001108976],"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":[0.000004423353,0.00001370732,3.475986e-7,0.0008357292,0.0001031529,0.00001647353,0.000006163668,0.02778917,0.0002225448,0.00003626111,0.9709502,0.00002179687],"study_design_scores_gemma":[0.0002210337,0.000007773659,0.0002412078,0.000108465,0.00004343315,0.00001428325,0.000003523963,0.002434796,0.00009120643,0.000108675,0.9962322,0.0004934086],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001165895,0.0001752152,0.00001309373,0.0002219626,0.002359922,0.0002200098,0.9961053,0.0006219856,0.0001658855],"genre_scores_gemma":[0.00005048478,0.0001739545,0.0001645908,0.0003373248,0.001088739,0.0001030073,0.9977722,0.0001178988,0.0001918187],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02614623,"threshold_uncertainty_score":0.9997486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02295997235412812,"score_gpt":0.2653774803176522,"score_spread":0.2424175079635241,"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."}}