{"id":"W4205798107","doi":"10.31234/osf.io/d9zbw","title":"Learning Children’s Conceptual Spaces using Deep Metric Learning.","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Categorization, perception, and language","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multidimensional scaling; Perception; Computer science; Task (project management); Set (abstract data type); Similarity (geometry); Cognition; Contrast (vision); Cognitive psychology; Space (punctuation); Cognitive development; Concept learning; Cognitive science; Artificial intelligence; Psychology; Data science; Machine learning; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001144903,0.00103219,0.0004043013,0.0008145773,0.0002523869,0.0009515914,0.001065768,0.0007533937,0.002373467],"category_scores_gemma":[0.005133612,0.0004410519,0.0008997825,0.0005720638,0.0005598982,0.002720367,0.001750757,0.002305274,0.0006162364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000800511,"about_ca_system_score_gemma":0.0006225149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003596279,"about_ca_topic_score_gemma":0.007025617,"domain_scores_codex":[0.999451,0.0001870292,0.00002527306,0.0002143323,0.00008309589,0.0000393769],"domain_scores_gemma":[0.9981761,0.001094232,0.0001593142,0.0003140204,0.0001515003,0.0001047969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002031017,0.0003128336,0.01321369,0.0004211719,0.0002426605,0.0002085683,0.001046153,0.1295896,0.0203965,0.03452969,0.01352108,0.786315],"study_design_scores_gemma":[0.00004183369,0.0001669445,0.004083896,0.00006174605,0.00002729071,0.0001212162,0.0003700895,0.9009206,0.007439938,0.07926872,0.007467668,0.00003008654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2111795,0.0009601115,0.7770911,0.0005979523,0.0001174533,0.0001306934,0.001207679,0.003048631,0.005666887],"genre_scores_gemma":[0.5468218,0.0003547171,0.4467226,0.0001586174,0.00001944857,0.0002161849,0.002787733,0.0001899913,0.002728897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003596279,"threshold_uncertainty_score":0.007939994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02857024616318917,"score_gpt":0.3238042910443472,"score_spread":0.295234044881158,"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."}}