{"id":"W6966306151","doi":"10.48448/y28t-x953","title":"Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images","year":2021,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Coherence (philosophical gambling strategy); Sentence; Code (set theory); Training set; Simple (philosophy); Text generation","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":[0.001458415,0.002072943,0.0007581168,0.002195412,0.001364044,0.00112344,0.002164681,0.001904052,0.009091329],"category_scores_gemma":[0.004814537,0.0004015767,0.001516282,0.001627529,0.0007962207,0.001951528,0.002869693,0.002076948,0.009327657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001116154,"about_ca_system_score_gemma":0.0009543165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007538456,"about_ca_topic_score_gemma":0.01800728,"domain_scores_codex":[0.9979464,0.0006186632,0.0001772535,0.0006277911,0.0004106046,0.0002193587],"domain_scores_gemma":[0.9982336,0.0004587022,0.0001194057,0.0005308134,0.0004490647,0.0002084489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002106798,0.00172478,0.006389571,0.003853553,0.0002791813,0.00110882,0.00213644,0.006673549,0.0332754,0.004677033,0.7343622,0.2034127],"study_design_scores_gemma":[0.0006651465,0.001006444,0.03573618,0.0007324955,0.000225165,0.002401982,0.00491975,0.05614918,0.05200836,0.006929274,0.8387846,0.0004414207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1310691,0.003724708,0.06226029,0.001558922,0.001587943,0.003487065,0.7268864,0.03884801,0.0305775],"genre_scores_gemma":[0.08596721,0.0003334245,0.06913584,0.0005070578,0.0001407581,0.002281918,0.8331355,0.0009185713,0.007579825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009091329,"threshold_uncertainty_score":0.03041351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02139262358139108,"score_gpt":0.298142384437435,"score_spread":0.2767497608560439,"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."}}