{"id":"W1993797592","doi":"10.1007/s10071-009-0296-y","title":"Features enhance the encoding of geometry","year":2009,"lang":"en","type":"article","venue":"Animal Cognition","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"National Institute of Mental Health","keywords":"Object (grammar); Heading (navigation); Encoding (memory); ENCODE; Artificial intelligence; Computer science; Representation (politics); Point (geometry); Computer vision; Transformation geometry; Task (project management); Group (periodic table); Geometric transformation; Process (computing); Silhouette; Transformation (genetics); Communication; Geometry; Mathematics; Image (mathematics); Psychology; Geography; Physics; Biology","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":[0.0001665328,0.0004325051,0.0003771213,0.0002160571,0.0001262578,0.001275292,0.0005020134,0.0005388062,0.005369748],"category_scores_gemma":[0.001922209,0.0002828478,0.0002811546,0.0002300062,0.0003780167,0.002172203,0.0007394359,0.0007159704,0.0004565768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003128245,"about_ca_system_score_gemma":0.0002182223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004860531,"about_ca_topic_score_gemma":0.000604442,"domain_scores_codex":[0.9998708,0.00001375758,0.000009262679,0.0000444264,0.0000397517,0.00002189489],"domain_scores_gemma":[0.9991329,0.0002223906,0.0002709712,0.0001995855,0.00008016704,0.00009397501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002327945,0.0003305295,0.008430672,0.0004848889,0.0001448145,0.0002655648,0.0003468226,0.002544828,0.8369721,0.009988501,0.002150508,0.1360128],"study_design_scores_gemma":[0.0006113423,0.006571233,0.3771414,0.0002706969,0.0008699149,0.002345267,0.0009305886,0.05032234,0.4282382,0.1091857,0.02323085,0.0002824863],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805701,0.0003319583,0.01199902,0.0001945317,0.0001274876,0.00001526423,0.0002393696,0.0001837471,0.00633836],"genre_scores_gemma":[0.9899913,0.0002928643,0.006107441,0.00008836245,0.00004759107,0.000008962263,0.0003672734,0.0001070241,0.002989176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005369748,"threshold_uncertainty_score":0.01796359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07249799013126278,"score_gpt":0.3301432790055084,"score_spread":0.2576452888742456,"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."}}