{"id":"W4405338202","doi":"10.5430/wjel.v15n2p309","title":"Attitude Behavior Tendencies and Knowledge Orientation as Antecedents of Maritime English Learning: Practical Implications for the International Maritime Industry","year":2024,"lang":"en","type":"article","venue":"World Journal of English Language","topic":"English Language Learning and Teaching","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Structural equation modeling; Context (archaeology); Computer science; Knowledge management; Orientation (vector space); Data collection; Psychology; Artificial intelligence; Machine learning; Mathematics; Statistics","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":[0.0012773,0.0001387182,0.0001913315,0.0003380579,0.0001603711,0.000496665,0.0006012612,0.0001032638,0.00005887086],"category_scores_gemma":[0.005386474,0.0001068364,0.0001337846,0.0003846932,0.00008175589,0.0008836053,0.0002188144,0.001224995,0.000002000916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007130504,"about_ca_system_score_gemma":0.0001795482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002462336,"about_ca_topic_score_gemma":0.0000449231,"domain_scores_codex":[0.9987183,0.0001208249,0.0004306782,0.0002337313,0.0002974472,0.000199019],"domain_scores_gemma":[0.9972349,0.001356364,0.0002501235,0.0002173102,0.00084332,0.00009792958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001259193,0.001002886,0.1193549,0.0003526579,0.001012326,0.0004299529,0.3460863,0.0003829916,0.004516142,0.2153785,0.0128217,0.2985357],"study_design_scores_gemma":[0.006084186,0.001749971,0.2101439,0.00279486,0.001468226,0.001906316,0.111424,0.01622452,0.00511496,0.003027063,0.638164,0.001898032],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8714094,0.01609392,0.06919155,0.004491642,0.01109689,0.0008790408,0.00006172582,0.000555105,0.02622075],"genre_scores_gemma":[0.98937,0.00006249062,0.007176784,0.00006279411,0.001284163,0.00002129819,0.000008999891,0.00001963392,0.001993807],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6253423,"threshold_uncertainty_score":0.6448501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01835670799453989,"score_gpt":0.3336846792296452,"score_spread":0.3153279712351053,"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."}}