{"id":"W2737523640","doi":"","title":"The common perceptual metric for human discrimination of number and density","year":2011,"lang":"en","type":"other","venue":"UCL Discovery (University College London)","topic":"Cognitive and developmental aspects of mathematical skills","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Numerosity adaptation effect; Metric (unit); Mathematics; Density estimation; Representation (politics); Contrast (vision); Context (archaeology); Perception; Pattern recognition (psychology); Variation (astronomy); Artificial intelligence; Statistics; Computer science; Physics; Psychology; Geography","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":[0.001015085,0.0004455602,0.0003613087,0.001051744,0.0003324974,0.001810994,0.0008296031,0.0008106225,0.005021002],"category_scores_gemma":[0.01018739,0.0002321616,0.0002676111,0.0007221038,0.002052689,0.003037523,0.001455749,0.0007540955,0.001088886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006497373,"about_ca_system_score_gemma":0.0002995632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001123483,"about_ca_topic_score_gemma":0.0009970507,"domain_scores_codex":[0.9991636,0.0001562577,0.00005348647,0.0002656117,0.0003169813,0.00004407505],"domain_scores_gemma":[0.9983256,0.000405545,0.0003535636,0.0003864348,0.000385369,0.0001434178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005002157,0.00008665299,0.03324446,0.0006214555,0.0001055629,0.0002132402,0.001844966,0.006973676,0.142751,0.3721539,0.01094701,0.4305578],"study_design_scores_gemma":[0.0000683481,0.0005460188,0.346211,0.0002922344,0.0000722652,0.004090826,0.001583777,0.06603441,0.05256268,0.4719739,0.0562372,0.0003273583],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2259008,0.001969523,0.6621615,0.002096383,0.0003228427,0.0001071118,0.001010787,0.001265687,0.1051654],"genre_scores_gemma":[0.8780451,0.0004815299,0.1168426,0.0002523194,0.00007042119,0.00006945297,0.0003641214,0.0001622669,0.003712266],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005021002,"threshold_uncertainty_score":0.01679695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02721769026419155,"score_gpt":0.2633762251640494,"score_spread":0.2361585348998579,"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."}}