{"id":"W3013281576","doi":"","title":"News Notes/En manchettes: Canadian camera helps mend FUSE","year":2006,"lang":"en","type":"article","venue":"JRASC","topic":"Cultural Industries and Urban Development","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Fuse (electrical); Political science; Computer science; Business; Artificial intelligence; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001697612,0.00009930207,0.0001066729,0.00004950803,0.0004812572,0.0001304874,0.0001807001,0.0001299573,0.002286797],"category_scores_gemma":[0.00007025053,0.00008881885,0.00004371013,0.0002518835,0.00008001574,0.0001224732,0.00002018791,0.000128168,0.0003019701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000493226,"about_ca_system_score_gemma":0.0004567544,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9187257,"about_ca_topic_score_gemma":0.9713777,"domain_scores_codex":[0.9989467,0.00004906747,0.0001485813,0.0001757218,0.0002296686,0.0004502093],"domain_scores_gemma":[0.9995216,0.00005133054,0.00003817138,0.0001115964,0.00004882998,0.0002284695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000186427,0.00001425878,0.01603207,0.000001935477,0.00001064963,0.00002414284,0.003574309,0.00001077448,0.00005232631,0.003108704,0.965142,0.01202694],"study_design_scores_gemma":[0.0001073878,0.000006998088,0.01360734,0.000007450677,0.000005590219,7.650211e-7,0.003029175,0.000003188933,0.0001271996,0.0002886152,0.9826642,0.0001520933],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4885359,0.0004028599,0.00002074162,0.139218,0.001100352,0.0004824659,0.00003750746,0.0001526697,0.3700495],"genre_scores_gemma":[0.8459024,0.00006046996,0.0002700393,0.001653103,0.0011771,0.00001770893,0.00003004191,0.00001197255,0.1508771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3573665,"threshold_uncertainty_score":0.9986252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02665053487627597,"score_gpt":0.265724692907085,"score_spread":0.239074158030809,"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."}}