{"id":"W7111786345","doi":"","title":"METIS: Multiple Extraction Techniques for Informative Sentences","year":2004,"lang":"en","type":"article","venue":"Research Explorer (The University of Manchester)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Pattern recognition (psychology); Feature extraction; Matching (statistics); Extraction (chemistry); Class (philosophy)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009771575,0.00007075688,0.0001203311,0.0002431339,0.0003931648,0.00006132696,0.0008271888,0.00003910482,0.0000142494],"category_scores_gemma":[0.00006252561,0.0000580864,0.0001111986,0.000415249,0.0001796793,0.001052704,0.0002653002,0.000140176,0.00003209644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008452737,"about_ca_system_score_gemma":0.00005969141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001785718,"about_ca_topic_score_gemma":0.000003795354,"domain_scores_codex":[0.9989353,0.00008971032,0.0001235949,0.0001878235,0.0004338154,0.0002297046],"domain_scores_gemma":[0.9989345,0.0003177712,0.00009627058,0.0003112513,0.0002889808,0.00005120632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003806789,0.0007688822,0.006009653,0.0003001748,0.0006430503,0.0000367311,0.5530847,0.001114791,0.01217034,0.04964762,0.009826674,0.3660167],"study_design_scores_gemma":[0.00409714,0.001500774,0.02170568,0.0006146195,0.00008470773,0.00001998736,0.4474556,0.1175732,0.2376353,0.01558,0.1528597,0.0008733796],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2033709,0.00008081111,0.7915816,0.003985725,0.00007386567,0.000542525,0.000002439963,0.00009204974,0.0002700768],"genre_scores_gemma":[0.9160196,0.0001815261,0.08367461,0.00003610347,0.00003541569,0.000004658702,0.000005159556,0.000003982028,0.00003897828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7126487,"threshold_uncertainty_score":0.3023945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1043092021438885,"score_gpt":0.3360963677354666,"score_spread":0.2317871655915781,"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."}}