{"id":"W2056014502","doi":"10.3917/ep.033.0140","title":"Sens, volumes et tracés","year":2006,"lang":"fr","type":"article","venue":"Cairn.info","topic":"French Urban and Social Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsemi (Canada)","funders":"","keywords":"TRAC; Environmental science; Computer science; Programming language","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":[0.001331515,0.000814085,0.0005279565,0.003961132,0.005368526,0.01209813,0.001117992,0.00139234,0.04917375],"category_scores_gemma":[0.004087435,0.0002711099,0.000329526,0.006456227,0.01273452,0.007238957,0.004774998,0.002278109,0.009411153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005259475,"about_ca_system_score_gemma":0.003305366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01092824,"about_ca_topic_score_gemma":0.0118869,"domain_scores_codex":[0.9980105,0.0008798692,0.00007977308,0.0002820519,0.0005598958,0.0001879554],"domain_scores_gemma":[0.998525,0.0006198181,0.0001321085,0.0003381656,0.0002221701,0.0001628895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003408408,0.0000171396,0.0008362487,0.0002295544,0.000006804611,0.0002268869,0.033755,0.0001003016,0.0001909202,0.8175178,0.09224097,0.05484427],"study_design_scores_gemma":[0.000001687432,0.000006572091,0.0004235464,0.0002013181,0.000002056515,0.0001297531,0.006461565,0.00002772022,0.00005450152,0.03068783,0.9619986,0.000004779083],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.005132245,0.01645823,0.002083249,0.007421166,0.002873307,0.00005307225,0.0004023147,0.0001359276,0.9654403],"genre_scores_gemma":[0.2146108,0.02465598,0.002617245,0.002775218,0.003681279,0.0002185892,0.0009276951,0.0005408164,0.7499723],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04917375,"threshold_uncertainty_score":0.1645026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02725513009787921,"score_gpt":0.2611410265307917,"score_spread":0.2338858964329125,"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."}}