{"id":"W6945124291","doi":"10.20383/103.01135","title":"Canadian Dip-In DAS (CanDiD) Project 1","year":2024,"lang":"en","type":"dataset","venue":"Federated Research Data Repository","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wireline; Tractor; Optical fiber; Sampling (signal processing); Strain gauge; Truck; Geophone; Wellbore","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":[0.002248855,0.003244811,0.001905082,0.006208628,0.002656204,0.003139664,0.005483265,0.002404141,0.03770072],"category_scores_gemma":[0.008159154,0.0008614353,0.002013742,0.01036353,0.0009066883,0.001306183,0.002970299,0.002210561,0.04883732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00784396,"about_ca_system_score_gemma":0.02083928,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7368095,"about_ca_topic_score_gemma":0.850567,"domain_scores_codex":[0.997367,0.0003727842,0.0001604001,0.0007394365,0.000821031,0.0005395027],"domain_scores_gemma":[0.9958617,0.0004919241,0.0002355291,0.0008105548,0.002076376,0.0005240002],"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":[0.0000989766,0.00002987277,0.001615456,0.0006835853,0.00006431422,0.00003377277,0.00004260164,0.0006369422,0.0001859509,0.0007343773,0.9917685,0.00410565],"study_design_scores_gemma":[0.0001800931,0.00002064025,0.007081843,0.0004119767,0.00006561735,0.00004255266,0.0001640197,0.001276047,0.0005379976,0.0010998,0.9890376,0.00008187008],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001873083,0.0001029249,0.0001216318,0.00007335831,0.00002319001,0.00002207865,0.9983432,0.0005169486,0.0006093819],"genre_scores_gemma":[0.0004042521,0.00006370658,0.0005759293,0.00003158632,0.000004618355,0.00007699005,0.9981982,0.00007973423,0.00056498],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2631905,"threshold_uncertainty_score":0.5294809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1644064456784795,"score_gpt":0.46068792465322,"score_spread":0.2962814789747405,"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."}}