{"id":"W7000482081","doi":"","title":"Exploitation du contenu visuel pour améliorer la recherche textuelle d’images en lignes","year":2010,"lang":"fr","type":"article","venue":"NPARC","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"General interest; Agrégation; Research methodology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002643818,0.0002593203,0.0002442699,0.0001361056,0.0001791232,0.0004866761,0.0008848698,0.0005749187,0.001190632],"category_scores_gemma":[0.002806889,0.0002495765,0.0001386697,0.000540379,0.0003170321,0.001024343,0.0002215396,0.001002706,0.001124545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008655525,"about_ca_system_score_gemma":0.0003056514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004912573,"about_ca_topic_score_gemma":0.000009275583,"domain_scores_codex":[0.9973687,0.000987522,0.000375393,0.0005220859,0.0003635657,0.0003827315],"domain_scores_gemma":[0.9962793,0.002138877,0.0001883677,0.0006287378,0.0005988177,0.0001658722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009136049,0.0001715906,0.000108867,0.00003828261,0.00001253991,0.00001737458,0.002029506,1.191694e-7,0.4894979,0.118285,0.03772298,0.3521067],"study_design_scores_gemma":[0.0004358901,0.0001353072,0.002527174,0.0001069245,0.00003245202,0.0001071654,0.000564604,0.01069106,0.5485372,0.141018,0.2953806,0.0004636851],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006968774,0.0006001695,0.7654871,0.08390506,0.001689476,0.0004197481,0.0000187201,0.0005859135,0.140325],"genre_scores_gemma":[0.5163831,0.0008897676,0.3635458,0.0007647391,0.0007888009,0.00009179911,0.00000765805,0.00004609985,0.1174822],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5094144,"threshold_uncertainty_score":0.9999956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1116494540020215,"score_gpt":0.3404426522192761,"score_spread":0.2287931982172545,"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."}}