{"id":"W2166141468","doi":"10.1093/llc/fqu061","title":"Citation segmentation from sparse & noisy data: A joint inference approach with Markov logic networks","year":2014,"lang":"en","type":"article","venue":"Digital Scholarship in the Humanities","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Excellence; Scholarship; Library science; Inference; Citation; Computer science; Artificial intelligence; Philosophy; Political science; Epistemology","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.006888499,0.0009648256,0.001632445,0.005171709,0.001172051,0.003229499,0.003069519,0.00219044,0.002083495],"category_scores_gemma":[0.02698983,0.001311623,0.002222074,0.004788768,0.00160085,0.005513869,0.00264915,0.003125538,0.0006307954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002836527,"about_ca_system_score_gemma":0.002269207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01401036,"about_ca_topic_score_gemma":0.01526518,"domain_scores_codex":[0.9961038,0.001787248,0.0002338292,0.000929341,0.0007160929,0.0002296517],"domain_scores_gemma":[0.975171,0.02109452,0.001466667,0.001059983,0.0009669828,0.0002407927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002284694,0.0001821312,0.007784036,0.0002777117,0.0003146545,0.0004114434,0.000879752,0.6765869,0.002263706,0.09587008,0.003496206,0.2117049],"study_design_scores_gemma":[0.000007238577,0.000009810883,0.0003409509,0.00001328582,0.00002117472,0.00002672496,0.00002185356,0.9503691,0.000427422,0.04787196,0.0008760701,0.00001431528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008153373,0.0003562726,0.9893959,0.0005269704,0.0000223952,0.00003583647,0.0002309409,0.0005109399,0.0007674462],"genre_scores_gemma":[0.3596891,0.0007064658,0.6333556,0.0004621849,0.0003968416,0.0002685042,0.001775778,0.0002919715,0.003053589],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9948283,"threshold_uncertainty_score":0.0364303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1734865759425414,"score_gpt":0.2799996284789401,"score_spread":0.1065130525363987,"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."}}