{"id":"W7145685724","doi":"","title":"OMTI All-Sky Imager Quick-Look Data (OI emission, 557.7nm, 5s) at Athabasca","year":2017,"lang":"en","type":"dataset","venue":"Institutional Repositories DataBase (IRDB)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Thermosphere; Filter (signal processing); Mesosphere; Data acquisition; Instrumentation (computer programming)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000655911,0.002799623,0.001504689,0.003036309,0.001272582,0.001631795,0.003736334,0.001841644,0.02719926],"category_scores_gemma":[0.001990375,0.0005808821,0.001330025,0.005993803,0.0005570989,0.001222399,0.001685344,0.001602534,0.04691367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002976743,"about_ca_system_score_gemma":0.004005483,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2719009,"about_ca_topic_score_gemma":0.4575246,"domain_scores_codex":[0.9992812,0.00006317285,0.00004371602,0.0001927199,0.0002376408,0.0001815372],"domain_scores_gemma":[0.9987901,0.000101839,0.00009617321,0.0002849839,0.0005916466,0.0001352319],"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":[0.00004726699,0.0000192137,0.0009292661,0.000271623,0.00002727736,0.00002275187,0.00001638829,0.0003974939,0.000176409,0.0002138838,0.9957248,0.002153614],"study_design_scores_gemma":[0.0001517908,0.00001827092,0.01342086,0.0003173472,0.00006040993,0.00008591692,0.0001511091,0.001474313,0.001064685,0.001085026,0.9820982,0.00007210772],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002611444,0.00006270896,0.00008197446,0.00003809012,0.00001848593,0.0000119884,0.998434,0.0003474781,0.0007441003],"genre_scores_gemma":[0.0003677316,0.00003961341,0.0001866554,0.00001716065,0.000003841215,0.00002612236,0.9987545,0.00003684489,0.0005674886],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7280991,"threshold_uncertainty_score":0.5406365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0569326866520369,"score_gpt":0.346088617058853,"score_spread":0.2891559304068161,"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."}}