{"id":"W2302211522","doi":"10.1037/cep0000062","title":"Numerical cognition: Adding it up.","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale","topic":"Cognitive and developmental aspects of mathematical skills","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Cognition; Field (mathematics); Context (archaeology); Cognitive science; Sketch; Numerical cognition; Neuropsychology; Cognitive psychology; Computer science; Psychology; Data science; Neuroscience; Algorithm; History; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001373157,0.0005676962,0.0003941239,0.002320311,0.001415534,0.006896804,0.0007831078,0.002100953,0.0116921],"category_scores_gemma":[0.008107151,0.0002675984,0.0004633695,0.001701987,0.01075844,0.01259462,0.003462974,0.003847413,0.002634393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001540247,"about_ca_system_score_gemma":0.001827906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002200617,"about_ca_topic_score_gemma":0.002848045,"domain_scores_codex":[0.9989175,0.0003228652,0.00005956485,0.0001875855,0.0004378455,0.00007475611],"domain_scores_gemma":[0.9974961,0.001026143,0.0001902695,0.0004748007,0.0005787978,0.0002339806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00004532269,0.00001652905,0.001737459,0.0002865329,0.00002044001,0.00008383283,0.003703849,0.0002072288,0.0004078455,0.817582,0.0256059,0.1503031],"study_design_scores_gemma":[0.000006181701,0.0000242793,0.003112641,0.0004535324,0.00001462007,0.0004492881,0.001546124,0.0003083963,0.0003103627,0.5941012,0.3996473,0.00002613268],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02550796,0.2186315,0.04988297,0.09725209,0.01076644,0.00003920547,0.0003493436,0.0004774246,0.5970931],"genre_scores_gemma":[0.6685157,0.1587779,0.04789146,0.02813549,0.01522795,0.0001375357,0.0005339736,0.0006015243,0.08017852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0116921,"threshold_uncertainty_score":0.039114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0808438913352893,"score_gpt":0.3540671347547135,"score_spread":0.2732232434194242,"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."}}