{"id":"W7025005265","doi":"","title":"Ãtude de la conversion de longueur d'onde","year":2000,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Diafiltration; Liquation; Emperipolesis; Triacetin; Fusible alloy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.004400911,0.001123327,0.0007918816,0.004096018,0.007223395,0.01171946,0.001937742,0.002590347,0.03359999],"category_scores_gemma":[0.0207589,0.000819802,0.001359433,0.003935712,0.003472083,0.003572951,0.00193153,0.004710994,0.006561252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02788996,"about_ca_system_score_gemma":0.0241655,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8482302,"about_ca_topic_score_gemma":0.7589458,"domain_scores_codex":[0.9933687,0.00137525,0.0002435224,0.0006883366,0.003658761,0.0006655027],"domain_scores_gemma":[0.991907,0.002416411,0.000317379,0.00104937,0.00400267,0.0003071312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008102483,0.000804916,0.02616507,0.0006475369,0.0001355909,0.002222014,0.01133832,0.006918393,0.0093272,0.143895,0.04418643,0.7535493],"study_design_scores_gemma":[0.0001666414,0.0002235809,0.08098961,0.0009197261,0.0001305172,0.004314894,0.01280116,0.01372859,0.02730437,0.007541887,0.8516667,0.0002123564],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3211415,0.0118371,0.04954387,0.0107798,0.001456605,0.0006685425,0.00394197,0.004672922,0.5959576],"genre_scores_gemma":[0.5929614,0.006566131,0.02484192,0.001487845,0.000274452,0.0002881429,0.003094807,0.001222275,0.3692631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8482302,"threshold_uncertainty_score":0.3053272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002459851744428148,"score_gpt":0.1597291895453272,"score_spread":0.1572693378008991,"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."}}