{"id":"W6950546351","doi":"10.5281/zenodo.8110985","title":"Neoascia tenur","year":2020,"lang":"fr","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Water Resources and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cégep de Rivière-du-Loup","funders":"","keywords":"Process (computing); Identification (biology); Product (mathematics)","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":[0.0002056963,0.0007576907,0.0003041209,0.001916081,0.002182591,0.0005397852,0.0004995858,0.0004151683,0.02660606],"category_scores_gemma":[0.0004281985,0.0001318186,0.0001551813,0.001028965,0.0009811429,0.000837094,0.001324487,0.0006103255,0.00488455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001164113,"about_ca_system_score_gemma":0.0006107728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01883037,"about_ca_topic_score_gemma":0.04242038,"domain_scores_codex":[0.9997645,0.00003569025,0.00001409616,0.00007282372,0.00007714352,0.00003577302],"domain_scores_gemma":[0.9998981,0.00001479195,0.00002599564,0.000008374429,0.00003830013,0.00001453993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004368998,0.0001114325,0.01061984,0.0009500526,0.00003709842,0.003368587,0.003248351,0.000712798,0.01616409,0.04134388,0.08243694,0.8405702],"study_design_scores_gemma":[0.0000236222,0.00007819126,0.0219166,0.0001758225,0.00001884095,0.001664156,0.0007996933,0.0001375736,0.0007417802,0.002530883,0.9718959,0.0000170896],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08641347,0.0253477,0.003663549,0.002669259,0.001925677,0.00044566,0.002376131,0.0007943782,0.8763641],"genre_scores_gemma":[0.7605755,0.009780213,0.004712692,0.00247521,0.000558653,0.0002038533,0.002544253,0.0001739758,0.2189756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02660606,"threshold_uncertainty_score":0.08900613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03742938754314562,"score_gpt":0.2113983183964592,"score_spread":0.1739689308533136,"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."}}