{"id":"W2027628524","doi":"10.3115/1599081.1599120","title":"Homotopy-based semi-supervised Hidden Markov Models for sequence labeling","year":2008,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sequence labeling; Hidden Markov model; Pattern recognition (psychology); Computer science; Artificial intelligence; Homotopy; TRACE (psycholinguistics); Markov chain; Semi-supervised learning; Sequence (biology); Mathematics; Machine learning; Task (project management)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001836743,0.0001568846,0.0001691322,0.00007330198,0.0003141344,0.00009133792,0.001014187,0.00006964912,0.00003099268],"category_scores_gemma":[0.00002042454,0.0001285703,0.00008058304,0.0002242376,0.00003864628,0.0009196117,0.0002431815,0.00008723241,0.00002268375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003288041,"about_ca_system_score_gemma":0.0001586272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008023582,"about_ca_topic_score_gemma":0.000002895643,"domain_scores_codex":[0.9986597,0.00002911699,0.0002185772,0.0004760491,0.0002804436,0.0003361494],"domain_scores_gemma":[0.9988109,0.0001360973,0.00005013885,0.000739558,0.0001358013,0.0001274774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001803696,0.0009603902,0.001225026,0.0003388293,0.0001058283,0.0003778703,0.002479544,0.03665443,0.05203734,0.1128429,0.09296617,0.6998314],"study_design_scores_gemma":[0.0005505965,0.00005262157,0.00002528591,0.00002578945,0.000002658501,0.00001242088,0.000006370285,0.9888691,0.004811491,0.003865934,0.001574357,0.0002033746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005889579,0.0001066995,0.9912464,0.0005363072,0.0001843344,0.0002902464,0.00002706157,0.0003107614,0.001408618],"genre_scores_gemma":[0.149228,0.00002343696,0.8489352,0.0009907094,0.00007409477,0.00005397517,0.00002809617,0.00001269532,0.0006538765],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9522147,"threshold_uncertainty_score":0.5242946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06524728755304249,"score_gpt":0.2699565562682337,"score_spread":0.2047092687151912,"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."}}