{"id":"W2999495378","doi":"","title":"日本語版Toronto Extremity Salvage Score(TESS)-上肢の開発-言語的妥当性を担保した翻訳版の作成 | 文献情報 | J-GLOBAL 科学技術総合リンクセンター","year":2016,"lang":"ja","type":"article","venue":"整形外科","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence","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":[0.001473055,0.0005571109,0.0005625578,0.002071473,0.0005726999,0.001026581,0.0004380957,0.0003356647,0.005173235],"category_scores_gemma":[0.005109868,0.0001395422,0.0008308376,0.001783457,0.0006892078,0.0007243818,0.0006046869,0.001026411,0.001308517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001210202,"about_ca_system_score_gemma":0.001881193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01309218,"about_ca_topic_score_gemma":0.02409704,"domain_scores_codex":[0.9989408,0.0001514696,0.000177658,0.0001102885,0.0004864159,0.0001333242],"domain_scores_gemma":[0.9964494,0.0004925636,0.0007504694,0.0001143197,0.001757451,0.0004358651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009424138,0.0001435116,0.8182983,0.0003457117,0.0008659431,0.000261178,0.0004306118,0.001794522,0.0007233818,0.001688576,0.03207169,0.1424342],"study_design_scores_gemma":[0.0001043266,0.0004949441,0.9553032,0.0003125312,0.0006904713,0.0008953588,0.0008157269,0.001909145,0.001006184,0.002943089,0.03542829,0.00009676641],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7317026,0.02083549,0.01330134,0.009002846,0.002912188,0.0005591245,0.02730677,0.0007200367,0.1936595],"genre_scores_gemma":[0.9746364,0.002183124,0.004350442,0.0005005683,0.0005799996,0.0001816919,0.006218184,0.00005549784,0.01129408],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01309218,"threshold_uncertainty_score":0.02603197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0194432656020179,"score_gpt":0.2910850871286315,"score_spread":0.2716418215266136,"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."}}