{"id":"W6931758559","doi":"10.5683/sp2/vagk02","title":"Okotoks Erratic -- Okotoks -- Laser Scanning -- Metadata -- 2016","year":2018,"lang":"en","type":"dataset","venue":"Borealis","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Metadata; Laser scanning; Laser; Point cloud; Cloud computing","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":[0.0006069712,0.002659188,0.001291408,0.002710701,0.0007417635,0.001585699,0.002560574,0.001746804,0.01834496],"category_scores_gemma":[0.002852984,0.0006673033,0.001593772,0.004382563,0.0006141906,0.001454151,0.00215462,0.001402446,0.04058847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001405966,"about_ca_system_score_gemma":0.002503173,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06801063,"about_ca_topic_score_gemma":0.1555202,"domain_scores_codex":[0.9992136,0.00005912932,0.00009364013,0.0002196854,0.00025365,0.0001603067],"domain_scores_gemma":[0.9989827,0.0001154701,0.0001126136,0.0003233695,0.0003409575,0.0001248376],"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":[0.0001714295,0.00003749291,0.003805616,0.001012018,0.00007423588,0.0000681657,0.00005629694,0.0008114672,0.0006406247,0.0003801988,0.9859958,0.006946661],"study_design_scores_gemma":[0.0002353018,0.00003530822,0.02265663,0.0004001998,0.00008195193,0.0002439648,0.0002222638,0.001415039,0.001695271,0.00141203,0.9715148,0.00008721533],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004861127,0.00008691898,0.0001294801,0.00003988559,0.00003373901,0.00001115034,0.9974782,0.001190835,0.0005436701],"genre_scores_gemma":[0.0006736728,0.00004570979,0.000273306,0.0000146668,0.000005248588,0.00002157257,0.9986196,0.00006315036,0.0002831012],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9319894,"threshold_uncertainty_score":0.1352295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02200372963565396,"score_gpt":0.2733316998216918,"score_spread":0.2513279701860378,"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."}}