{"id":"W999465353","doi":"","title":"Adaptation to Near Addition Lens","year":2006,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adaptation (eye); Lens (geology); Optometry; Through-the-lens metering; Optics; Physics; Medicine","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":[0.0004496737,0.0001450539,0.0001513751,0.0001998736,0.0003452161,0.00009313352,0.0001477608,0.0001106776,0.00009574964],"category_scores_gemma":[0.0001656516,0.0001414111,0.00003395856,0.001219802,0.0005897243,0.0004566353,0.0000336243,0.0001533853,0.0003830735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001735247,"about_ca_system_score_gemma":0.0000954917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004098473,"about_ca_topic_score_gemma":0.00001037817,"domain_scores_codex":[0.9987136,0.00006229313,0.0002538257,0.0003126038,0.0003129272,0.0003447272],"domain_scores_gemma":[0.9994851,0.00005933995,0.00004954585,0.0001282566,0.0001467227,0.0001310685],"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":[0.00000903446,0.0000194409,0.0006028556,0.000006060799,0.000004374477,0.00002790023,0.0004557419,0.04015351,0.9560354,0.0002993083,0.00107197,0.001314423],"study_design_scores_gemma":[0.0004281464,0.0008951353,0.06746224,0.00008230734,0.00001174014,0.0002612385,0.0003938976,0.07019732,0.8514254,0.003827901,0.004431614,0.0005830486],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912833,0.00001649601,0.0003192435,0.00005248407,0.0008678781,0.0002534892,0.0000102859,0.0001807009,0.007016134],"genre_scores_gemma":[0.9984099,2.77165e-7,0.001131115,0.00004984145,0.0002323719,0.00004521142,0.000007708598,0.00001462313,0.0001089585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.10461,"threshold_uncertainty_score":0.5766578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0468575267562669,"score_gpt":0.2965717876925691,"score_spread":0.2497142609363022,"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."}}