{"id":"W2592420180","doi":"10.5220/0006256507380745","title":"Dynamic Selection of Exemplar-SVMs for Watch-list Screening through Domain Adaptation","year":2017,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Université du Québec","funders":"","keywords":"Domain adaptation; Support vector machine; Computer science; Selection (genetic algorithm); Artificial intelligence; Adaptation (eye); Machine learning; Domain (mathematical analysis); Pattern recognition (psychology); Mathematics; Classifier (UML); Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002609887,0.0000745775,0.0001090457,0.00002158964,0.0003238852,0.0001397559,0.0005385312,0.00004909043,0.000007347384],"category_scores_gemma":[0.00004491205,0.00006915769,0.0000516666,0.00004471425,0.00002433664,0.0008749035,0.0001044682,0.00005055006,0.000001925469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002606225,"about_ca_system_score_gemma":0.00002619381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006258474,"about_ca_topic_score_gemma":0.0004826902,"domain_scores_codex":[0.9992388,0.00001681479,0.0001852012,0.0002493909,0.0001495358,0.0001602088],"domain_scores_gemma":[0.9992155,0.0000423466,0.0001675448,0.0004572987,0.0000938102,0.0000235007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005220169,0.00008907134,0.004522263,0.0001169425,0.00006740927,0.000002266709,0.005590788,0.01871591,0.02120537,0.7179281,0.0004783453,0.2312314],"study_design_scores_gemma":[0.0003417392,0.00004555611,0.001886753,0.00001930011,0.000003714092,0.00000347885,0.00007735388,0.9672158,0.003115334,0.02650705,0.0006931813,0.00009077741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03741419,0.000008751636,0.9591666,0.001095534,0.0002038712,0.0001807425,0.000001185545,0.00007460362,0.001854453],"genre_scores_gemma":[0.5125362,0.000001375494,0.4869864,0.00002946321,0.00001800674,0.000008868253,0.000001352451,0.000003475856,0.0004148978],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9484999,"threshold_uncertainty_score":0.2820169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05166690286437465,"score_gpt":0.3054666274369633,"score_spread":0.2537997245725886,"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."}}