{"id":"W2997835294","doi":"","title":"A Selective Sampling Strategy for Label Ranking","year":2006,"lang":"en","type":"article","venue":"","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Automatic summarization; Ranking (information retrieval); Generalization; Sampling (signal processing); Heuristic; Set (abstract data type); Context (archaeology); Artificial intelligence; Machine learning; Extension (predicate logic); Ranking SVM; Data mining","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":[0.004636011,0.001000426,0.001506431,0.001816522,0.0007113578,0.001453074,0.003615713,0.001834786,0.003526197],"category_scores_gemma":[0.01580933,0.0005544907,0.0006650476,0.00136228,0.001576294,0.002905988,0.002081699,0.001864885,0.001054021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009981099,"about_ca_system_score_gemma":0.001334665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001365196,"about_ca_topic_score_gemma":0.00223207,"domain_scores_codex":[0.9963688,0.001879983,0.0001246615,0.0004916094,0.0009661212,0.0001687489],"domain_scores_gemma":[0.9913403,0.005635336,0.0003963213,0.001415793,0.0009436787,0.0002685701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004099866,0.0005445234,0.002859862,0.0002127776,0.000142216,0.0001862162,0.000517502,0.2235486,0.01467084,0.1979309,0.007150482,0.5518261],"study_design_scores_gemma":[0.00005479784,0.0001171332,0.000220988,0.00001683623,0.00002016113,0.00007213187,0.00003275894,0.9395013,0.00377037,0.05398806,0.002187324,0.00001806812],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006139732,0.00008271784,0.9922366,0.0001458359,0.0000138236,0.00009723657,0.00004182451,0.0001988152,0.001043369],"genre_scores_gemma":[0.3174123,0.0001949777,0.677034,0.0004598203,0.0001948081,0.0007963612,0.0005166456,0.0001509605,0.003240107],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004636011,"threshold_uncertainty_score":0.02451783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09515485482992776,"score_gpt":0.2983533436185219,"score_spread":0.2031984887885942,"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."}}