{"id":"W6964008608","doi":"10.25318/3410001601-fra","title":"Dépenses en immobilisations et réparations, extraction minière et extraction de pétrole et de gaz","year":2019,"lang":"fr","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Robotics and Automated Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Extraction (chemistry); Solvent extraction; Accelerated solvent extraction","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"],"consensus_categories":[],"category_scores_codex":[0.001144624,0.0005659991,0.0004935838,0.0002852027,0.0002413541,0.0003972095,0.000257474,0.0005322339,0.0003382865],"category_scores_gemma":[0.001044504,0.0007173598,0.00007154125,0.0003071073,0.00003647604,0.0004977213,0.00003446406,0.0009156254,0.0001014241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0029936,"about_ca_system_score_gemma":0.002563746,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1389633,"about_ca_topic_score_gemma":0.5452506,"domain_scores_codex":[0.9965495,0.0006973196,0.0009379378,0.0005024452,0.0007125589,0.0006001692],"domain_scores_gemma":[0.9958045,0.002521579,0.0005479414,0.0005452883,0.0003491355,0.0002315196],"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":[0.000009548863,0.0001022914,0.0001000634,0.0005823082,0.0001107683,0.00005456808,0.0005222296,0.2043768,0.004305946,0.01163642,0.7774085,0.0007905877],"study_design_scores_gemma":[0.0005014532,0.0001358089,0.05480163,0.001323799,0.0005876407,0.0002951134,0.002315182,0.2136051,0.001373315,0.0005891183,0.7230537,0.001418132],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001076277,0.0003596759,0.2866068,0.0008361196,0.002676205,0.0006720722,0.7071795,0.00007647913,0.000516763],"genre_scores_gemma":[0.05858906,0.001255992,0.02154409,0.0001943658,0.0002489199,0.0001825339,0.9152129,0.0001281165,0.002644058],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4062873,"threshold_uncertainty_score":0.9995278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01024578403350624,"score_gpt":0.3121784664889285,"score_spread":0.3019326824554223,"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."}}