{"id":"W1574451148","doi":"10.1007/978-3-540-27835-1_22","title":"Anatomy and Physiology of an Artificial Vision Matrix","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Snapshot (computer storage); Artificial vision; Image processing; Artificial intelligence; Computer vision; Visual processing; Information processing; Vision science; Machine vision; Neuroscience; Image (mathematics); Biology; Perception","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":[0.0001345844,0.0002119326,0.0001614984,0.0006416631,0.0004593937,0.001878128,0.0007283627,0.0007266183,0.009222022],"category_scores_gemma":[0.0006950784,0.0002673709,0.000203796,0.0004749715,0.001224773,0.00202567,0.0006504231,0.0009161958,0.00193424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003651076,"about_ca_system_score_gemma":0.000560069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000909465,"about_ca_topic_score_gemma":0.0006587382,"domain_scores_codex":[0.9998686,0.0000187012,0.000005608997,0.00002968529,0.00006601313,0.00001134699],"domain_scores_gemma":[0.9997647,0.00007934005,0.00002284052,0.0000303768,0.00006984782,0.00003279539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002555885,0.000007892551,0.0001506655,0.00005569346,0.000004935986,0.0002046137,0.0001816468,0.002037402,0.03411642,0.9297364,0.002455027,0.03102372],"study_design_scores_gemma":[0.00002288651,0.0001133831,0.003170281,0.00005438425,0.00001423353,0.003310073,0.0002624468,0.04324728,0.01720835,0.8005146,0.1320302,0.0000520045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0609309,0.005336996,0.6608462,0.003163511,0.0005555287,0.00006173742,0.0003924084,0.0009206058,0.2677921],"genre_scores_gemma":[0.6560255,0.003941864,0.241291,0.000388141,0.000313811,0.000107139,0.0002574459,0.0001965723,0.09747858],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009222022,"threshold_uncertainty_score":0.03085077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03331421406001032,"score_gpt":0.3363331034768943,"score_spread":0.3030188894168839,"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."}}