{"id":"W3200775831","doi":"10.31219/osf.io/tsjxr","title":"Generating Compositional Color Representations from Text","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Pipeline (software); Artificial intelligence; Discriminative model; Generative grammar; Set (abstract data type); Pattern recognition (psychology); Object (grammar); Natural language processing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003612044,0.0008164583,0.0003916115,0.0007958323,0.0002819636,0.0006696804,0.000965962,0.0007509589,0.003628958],"category_scores_gemma":[0.001878528,0.0002735182,0.0007510143,0.0008217291,0.0004911478,0.001729443,0.000814279,0.0008668256,0.001546535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006795831,"about_ca_system_score_gemma":0.0004125164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002339265,"about_ca_topic_score_gemma":0.003291368,"domain_scores_codex":[0.9997821,0.0000418352,0.000006939974,0.00008318202,0.00004864237,0.0000372847],"domain_scores_gemma":[0.9995259,0.000191875,0.0000481193,0.0001131022,0.00008688818,0.00003417735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009538316,0.0005160268,0.004588047,0.0003971629,0.0001382088,0.0006672567,0.0003213067,0.3060762,0.146229,0.03687912,0.02053157,0.4827023],"study_design_scores_gemma":[0.00001792758,0.00006725892,0.0004438746,0.000007841561,0.00001095044,0.00008164738,0.00002841427,0.9666158,0.01849554,0.01197034,0.002247707,0.00001264476],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1535314,0.0003602004,0.8273226,0.0005238077,0.0001562484,0.0001769967,0.001666753,0.009480212,0.00678186],"genre_scores_gemma":[0.729381,0.000316246,0.2533321,0.0003952336,0.0001049052,0.0001754302,0.004149614,0.0006348119,0.01151063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003628958,"threshold_uncertainty_score":0.0121401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03760655398263808,"score_gpt":0.313027063731404,"score_spread":0.275420509748766,"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."}}