{"id":"W7133512147","doi":"10.7202/1123659ar","title":"INTRODUCTION","year":2025,"lang":"fr","type":"article","venue":"Diversité urbaine","topic":"Cultural Insights and Digital Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Telmatology; Pipeline (software); Context (archaeology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007254096,0.0001408325,0.0001386396,0.00009380412,0.0001842723,0.000666817,0.0003227955,0.00008122849,0.0003780423],"category_scores_gemma":[0.00007326703,0.0001238874,0.00008859146,0.0007016714,0.000107324,0.002142201,0.0004646848,0.0001185683,0.0005727815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001136486,"about_ca_system_score_gemma":0.00005879054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004128468,"about_ca_topic_score_gemma":0.00002350915,"domain_scores_codex":[0.999086,0.00002920733,0.0001302301,0.0003311316,0.0001423194,0.0002811106],"domain_scores_gemma":[0.9993222,0.00002878695,0.00004307534,0.000318935,0.0001783432,0.0001086454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007035658,0.00006568793,0.00009531291,0.00002110668,0.00003387381,0.00004369559,0.001175827,0.0000341454,0.0001428716,0.5171462,0.4577164,0.02351787],"study_design_scores_gemma":[0.0003069515,0.00006945465,0.008151447,0.00004545333,0.00003510664,0.00002411162,0.00005448456,0.001848759,0.001054037,0.01096017,0.9773015,0.0001485014],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.009111989,0.01931961,0.04345682,0.3480406,0.01577602,0.000239496,0.00002739428,0.0002239293,0.5638041],"genre_scores_gemma":[0.1568973,0.0001159766,0.0006946236,0.002358285,0.0009860116,7.397922e-7,0.00001390263,0.000003260296,0.83893],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.5195851,"threshold_uncertainty_score":0.7362139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08544404624509176,"score_gpt":0.258591120327857,"score_spread":0.1731470740827653,"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."}}