{"id":"W4415465301","doi":"10.1017/psy.2026.10103","title":"High-Dimensional Perception with the Double Machine Learning Lens Model","year":2025,"lang":"en","type":"article","venue":"Psychometrika","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Perception; Artifact (error); Class (philosophy); Through-the-lens metering; Perceptual learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000278255,0.0001310079,0.0001479017,0.000351385,0.0001955377,0.00005610693,0.00008382582,0.00009977913,0.00007876443],"category_scores_gemma":[0.00001501669,0.00008371111,0.00005252305,0.001085453,0.00001787041,0.00009904667,0.00001340291,0.0003655553,0.00007132153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007221481,"about_ca_system_score_gemma":0.00001277401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008960405,"about_ca_topic_score_gemma":0.00002457578,"domain_scores_codex":[0.9992638,0.00002587515,0.0001591889,0.0001639527,0.0002128099,0.0001743418],"domain_scores_gemma":[0.9996369,0.00006771406,0.00002798979,0.0001868222,0.00005293214,0.00002766561],"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":[0.0002560258,0.00002735054,0.0007121268,0.00003101703,0.0000968172,0.000001067961,0.0001496591,0.9487626,0.008964688,0.0006555152,0.01378805,0.0265551],"study_design_scores_gemma":[0.004936145,0.0002421563,0.006895476,0.0001442829,0.00007735622,0.00001933986,0.0002001842,0.9081155,0.002258201,0.0001366018,0.07649513,0.0004796241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9253408,0.000422568,0.06107014,0.0003761016,0.001252043,0.0003413935,0.000006225899,0.0004659359,0.01072477],"genre_scores_gemma":[0.99638,0.00002518081,0.000298628,0.00007262336,0.0001307534,0.00002701847,0.000008254997,0.00002067653,0.003036873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07103917,"threshold_uncertainty_score":0.341364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01985800634913224,"score_gpt":0.2386472495717296,"score_spread":0.2187892432225974,"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."}}