{"id":"W2894029453","doi":"10.1167/18.10.206","title":"Title: Convolutional Network Approach to Modelling Allocentric Landmark Impact on Target Localization","year":2018,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Landmark; Artificial intelligence; Affine transformation; Computer vision; Computer science; Pattern recognition (psychology); Transformation (genetics); Transformation matrix; Mathematics; Geometry; Physics","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":[0.0001098506,0.0000414454,0.00006741821,0.00003396058,0.00002846744,0.00001037808,0.00004523736,0.00002384288,0.00007315359],"category_scores_gemma":[0.000006650242,0.00002965299,0.00003655483,0.0001486329,0.000005609802,0.00002715209,0.000005109291,0.00006417276,0.00009579631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002894413,"about_ca_system_score_gemma":0.000007481559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.827894e-7,"about_ca_topic_score_gemma":1.272851e-8,"domain_scores_codex":[0.9996794,0.00001064816,0.0001035207,0.00003591269,0.00009814295,0.00007237338],"domain_scores_gemma":[0.9997927,0.00002092571,0.00002655306,0.00004593396,0.00005897567,0.0000549259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001006914,0.00002648905,0.00002645585,0.000003115908,0.000008680538,1.533324e-7,0.00001289641,0.953113,0.0002049087,0.0009418804,0.04312696,0.002525418],"study_design_scores_gemma":[0.00007118914,0.00009083319,0.00174954,0.0000231482,0.000004920311,0.000002540314,0.000001510945,0.9707978,0.00004820568,0.002240292,0.02492951,0.00004046477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009180358,0.00006504712,0.9819121,0.00002908249,0.0001562037,0.00003592128,0.000001630162,0.00001149838,0.008608172],"genre_scores_gemma":[0.9365454,0.00001502259,0.06252118,0.00003901763,0.0008258965,5.96364e-7,0.000002552097,0.000008007491,0.00004227753],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9273651,"threshold_uncertainty_score":0.12313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01552077260762296,"score_gpt":0.2854806366709888,"score_spread":0.2699598640633659,"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."}}