{"id":"W4380520997","doi":"10.6000/1929-4409.2020.09.270","title":"Principles Behind Semantic Relation between Common Abbreviations and their Expansions on Instagram","year":2022,"lang":"en","type":"article","venue":"International Journal of Criminology and Sociology","topic":"Information Retrieval and Data Mining","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Novelty; Relation (database); Meaning (existential); Computer science; Phenomenon; Semantic relation; Information retrieval; Natural language processing; Epistemology; Psychology; Data mining; Philosophy; Cognition","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000476634,0.0000729876,0.000163764,0.0002460773,0.00030015,0.00003006298,0.0004011731,0.00006870873,0.00001953243],"category_scores_gemma":[0.0001121868,0.00005909923,0.00003936888,0.00001534942,0.0001400183,0.000361104,0.0003241228,0.0003851266,0.000002387825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004074943,"about_ca_system_score_gemma":0.00006026507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004775772,"about_ca_topic_score_gemma":5.980225e-7,"domain_scores_codex":[0.9991,0.0001793969,0.0003804145,0.00009993166,0.0001408401,0.00009946588],"domain_scores_gemma":[0.9989036,0.0004922888,0.000354848,0.00008751188,0.0001218436,0.00003996272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00004175472,0.00005046772,0.01740179,0.000004332861,0.0001389663,0.00002201692,0.0165782,0.000311496,0.0001169044,0.9205265,0.0001448744,0.04466267],"study_design_scores_gemma":[0.002019451,0.001783444,0.6750847,0.00003921766,0.0000527999,0.001699377,0.002328437,0.008885811,0.0003467523,0.265828,0.04161033,0.0003216904],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9668303,0.0001607184,0.02636537,0.005547023,0.0005447277,0.00004236277,0.00002163405,0.00001255263,0.0004753759],"genre_scores_gemma":[0.9978301,0.00008634316,0.001014233,0.0009285224,0.00008418223,0.000002738864,0.00002512734,0.000002299018,0.00002647544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.657683,"threshold_uncertainty_score":0.2409997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08582433621905634,"score_gpt":0.3160701287923134,"score_spread":0.2302457925732571,"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."}}