{"id":"W2035911806","doi":"10.1167/5.8.481","title":"Spatiotemporal templates for detecting 1st- and 2nd-order orientation- and luminance-defined targets","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Luminance; Artificial intelligence; Computer science; Computer vision; Orientation (vector space); Stimulus (psychology); Frame (networking); Pattern recognition (psychology); Template; Mathematics; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000620803,0.00009660713,0.0001721159,0.0001426228,0.000105313,0.00004787173,0.00004532772,0.00009399979,0.00001332565],"category_scores_gemma":[0.0009316803,0.00008233859,0.000033372,0.00009125641,0.00003023143,0.000320618,0.00001544939,0.0002521604,5.508649e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001392028,"about_ca_system_score_gemma":0.00001213204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003773066,"about_ca_topic_score_gemma":0.00000826978,"domain_scores_codex":[0.9993643,0.00002285143,0.0003056536,0.00008443486,0.0001020213,0.0001207221],"domain_scores_gemma":[0.9992031,0.000351898,0.0001521048,0.00006446928,0.0001795308,0.00004884359],"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":[0.0001250913,0.00001296784,0.006822397,0.0001392266,0.00003878081,0.000007756119,0.0007664035,0.001476614,0.8881412,0.00007693731,0.0004234335,0.1019692],"study_design_scores_gemma":[0.003260561,0.001321398,0.184856,0.0001884405,0.00008081886,0.0006400839,0.0008795098,0.05335894,0.7345549,0.009985342,0.01029952,0.0005744738],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9565426,0.0003547945,0.04163213,0.00007626246,0.001215263,0.00009719827,0.000002582023,0.00003851643,0.00004070482],"genre_scores_gemma":[0.6616755,0.00006239847,0.3380739,0.000009339876,0.0001497502,0.00000228137,6.110524e-7,0.00001465576,0.0000116265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2964418,"threshold_uncertainty_score":0.335767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01721992382277902,"score_gpt":0.2932392925767085,"score_spread":0.2760193687539295,"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."}}