{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000177836,0.0001574546,0.0001745947,0.0000990066,0.0003350218,0.000416688,0.001274561,0.00009671076,0.001749944],"category_scores_gemma":[0.00002237761,0.0001585669,0.0001220135,0.0002100793,0.00003966431,0.0001772966,0.002468633,0.0004579013,0.00005763775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001253605,"about_ca_system_score_gemma":0.0002151416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003232484,"about_ca_topic_score_gemma":0.000004889281,"domain_scores_codex":[0.9982798,0.0001482263,0.0003355771,0.0006517433,0.0004415775,0.0001430932],"domain_scores_gemma":[0.9985763,0.0001739192,0.0001971944,0.000856547,0.0001354737,0.00006057209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000183425,0.0006802263,0.0009163506,0.00006298941,0.0003577602,0.0000702805,0.001697169,0.004670752,0.111607,0.7680704,0.06193382,0.04991492],"study_design_scores_gemma":[0.0002398204,0.00005784911,0.003843095,0.00003384327,0.00003600762,0.00001063794,0.0001457895,0.8376985,0.06471663,0.07286534,0.01958251,0.0007699679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00199188,0.0001327078,0.9772553,0.003770114,0.0004998762,0.0002938838,0.0001144842,0.0007601816,0.01518155],"genre_scores_gemma":[0.09551845,0.00004772126,0.8961906,0.001540378,0.0002647972,0.0004713544,0.0009720897,0.00001712251,0.004977514],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8330278,"threshold_uncertainty_score":0.9991626,"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."}}