{"id":"W1793630844","doi":"","title":"Complete Complimentary Results Report of the MARF's NLP Approach to the DEFT 2010 Competition","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Competition (biology); Identification (biology); Artificial intelligence; Natural language processing; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01984225,0.004966388,0.003273009,0.006099464,0.004491181,0.00696132,0.003197216,0.003738763,0.06241198],"category_scores_gemma":[0.03987598,0.001123166,0.002441179,0.00420137,0.001789107,0.005390291,0.006366484,0.004112883,0.07387547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004225789,"about_ca_system_score_gemma":0.005459376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04797639,"about_ca_topic_score_gemma":0.07403103,"domain_scores_codex":[0.9730397,0.008186777,0.00133401,0.003844794,0.01121992,0.002374866],"domain_scores_gemma":[0.9673209,0.008917196,0.0006592888,0.007914121,0.01166508,0.003523343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001016274,0.0008623812,0.001747176,0.0006861304,0.0003011533,0.0003160525,0.0002913105,0.003629194,0.004591011,0.001405826,0.9266995,0.05845396],"study_design_scores_gemma":[0.001548233,0.001659913,0.0296722,0.0002588276,0.0004156876,0.001586645,0.001436481,0.03725103,0.02351053,0.007548912,0.894558,0.0005536597],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1072932,0.009096927,0.04586711,0.01202167,0.01709042,0.002738353,0.521669,0.06583195,0.2183914],"genre_scores_gemma":[0.09438556,0.0008591676,0.03649225,0.002342224,0.001336515,0.001866169,0.7762789,0.009386208,0.07705303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06241198,"threshold_uncertainty_score":0.2087889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06115094374699705,"score_gpt":0.2009543871537144,"score_spread":0.1398034434067174,"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."}}