{"id":"W2154109570","doi":"10.5539/elt.v4n1p70","title":"The Effect of Collocation on Meaning Representation of Adjectives such as Big and Large in Translation from Two Languages Used in the Article to English Language Texts","year":2011,"lang":"en","type":"article","venue":"English Language Teaching","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Linguistics; Meaning (existential); Collocation (remote sensing); Noun; Psychology; Representation (politics); Computer science; Philosophy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01457316,0.001022405,0.000576245,0.001438961,0.00238819,0.003934744,0.001011312,0.001452148,0.0141289],"category_scores_gemma":[0.1608122,0.0008578835,0.0006596693,0.001403912,0.004181745,0.0063218,0.004186836,0.003102355,0.002342804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001553119,"about_ca_system_score_gemma":0.0009330863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005376463,"about_ca_topic_score_gemma":0.004519271,"domain_scores_codex":[0.9806786,0.01441871,0.001121164,0.001734317,0.00165357,0.0003936856],"domain_scores_gemma":[0.7192042,0.2463548,0.009493952,0.01228319,0.01067648,0.001987351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.02391899,0.001341363,0.1017212,0.004075167,0.0007144785,0.004156359,0.1123614,0.01107972,0.355565,0.03448833,0.01221484,0.3383632],"study_design_scores_gemma":[0.00226348,0.007159022,0.5593465,0.001486299,0.00315427,0.009018758,0.06198721,0.08929683,0.1397668,0.07197942,0.05352306,0.001018373],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9249402,0.0008462271,0.02580185,0.001679769,0.0005966541,0.0002577551,0.0001940231,0.0008004838,0.04488304],"genre_scores_gemma":[0.9850346,0.0002198099,0.009454172,0.0004710536,0.00006380832,0.00009621537,0.0001768032,0.0006140124,0.003869508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01457316,"threshold_uncertainty_score":0.07707113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01228195642688663,"score_gpt":0.3020181871764583,"score_spread":0.2897362307495717,"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."}}