{"id":"W2319304088","doi":"10.3166/ts.28.547-574","title":"Sélection adaptative de caractéristiques pertinentes et classification hiérarchique des images dans les bases hétérogènes","year":2011,"lang":"fr","type":"article","venue":"Traitement du signal","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Political science","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"],"consensus_categories":[],"category_scores_codex":[0.001101246,0.0004278125,0.0002989038,0.0002235348,0.0004973235,0.0003981464,0.0006496201,0.0001807115,0.0003516912],"category_scores_gemma":[0.0001616083,0.0004186531,0.0002087062,0.0005001049,0.0007928871,0.001576793,0.0001176506,0.0003834452,0.00002938801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003996224,"about_ca_system_score_gemma":0.0003721572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002925353,"about_ca_topic_score_gemma":0.0002613814,"domain_scores_codex":[0.9965966,0.001014865,0.0006665647,0.0006845494,0.0004345051,0.0006029186],"domain_scores_gemma":[0.9981282,0.0002809205,0.0004075789,0.0003575786,0.0006071466,0.0002185141],"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":[0.0002115982,0.00176474,0.01642282,0.0003089991,0.0001586522,0.00004439805,0.05388968,0.00001166354,0.4103185,0.2158805,0.00244298,0.2985455],"study_design_scores_gemma":[0.000364752,0.0009237964,0.3117029,0.0003858721,0.0001091936,0.00008647441,0.003066602,0.03938629,0.6239323,0.01282527,0.006607505,0.0006090283],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02984378,0.002011582,0.9544563,0.0033469,0.0001799327,0.0005095516,0.00006676909,0.0005331972,0.009052016],"genre_scores_gemma":[0.8968655,0.001567074,0.09816208,0.000296062,0.000195254,0.0002036187,0.00003469098,0.00003807513,0.002637679],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8670217,"threshold_uncertainty_score":0.9998266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1716398442296027,"score_gpt":0.3201233182511283,"score_spread":0.1484834740215256,"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."}}