{"id":"W2175506846","doi":"10.22230/src.2012v3n1a47","title":"Drilling for Papers in INKE","year":2012,"lang":"en","type":"article","venue":"Scholarly and Research Communication","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Citation; Computer science; Chaining; Process (computing); Forward chaining; Plan (archaeology); Domain (mathematical analysis); Interface (matter); Animation; Listing (finance); World Wide Web; Data science; Operations research; Artificial intelligence; Computer graphics (images); Expert system; Engineering; Programming language; History; Business; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004560532,0.0009824422,0.0008019919,0.007175091,0.002553255,0.006607297,0.001385919,0.001649702,0.09807614],"category_scores_gemma":[0.04416262,0.0006933387,0.000886023,0.004398164,0.000851721,0.01319938,0.006249146,0.001096844,0.02558098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004898504,"about_ca_system_score_gemma":0.0007482953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004427573,"about_ca_topic_score_gemma":0.0009892263,"domain_scores_codex":[0.9975573,0.0005843732,0.0003415504,0.0003924588,0.0009716059,0.0001527912],"domain_scores_gemma":[0.9690021,0.01885629,0.001873487,0.005116752,0.003466059,0.0016852],"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":[0.002153779,0.0003507274,0.01084041,0.002364579,0.000105013,0.002291351,0.01439887,0.0007811183,0.0148718,0.03923156,0.2179231,0.6946878],"study_design_scores_gemma":[0.0001730465,0.0002373566,0.002957891,0.000485513,0.00006649846,0.0008979956,0.00438761,0.004212503,0.0113212,0.01934225,0.9557934,0.0001247704],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0923715,0.003866376,0.4784254,0.01142942,0.005903872,0.003008559,0.008515447,0.1387105,0.2577689],"genre_scores_gemma":[0.2669453,0.001855809,0.5592858,0.003468282,0.001009116,0.001798144,0.007525284,0.02390469,0.1342076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9933927,"threshold_uncertainty_score":0.3280973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1351611189471177,"score_gpt":0.400641904917846,"score_spread":0.2654807859707283,"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."}}