{"id":"W7143778557","doi":"10.1002/j.1538-9235.2001.tb01309.x","title":"Compensating for Vertical Anisometropic Imbalance by the Positioning of Segment Centers","year":2001,"lang":"en","type":"article","venue":"Optometry and Vision Science","topic":"Advanced optical system design","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Offset (computer science); Point (geometry); Reading (process); Horizontal and vertical; Spectacle","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":[0.0005394565,0.0005162241,0.0004298487,0.0006286492,0.0004804458,0.0007035609,0.0006814048,0.000432051,0.001801335],"category_scores_gemma":[0.002191698,0.0002950786,0.0002785728,0.0007743443,0.0003878374,0.0007129778,0.0004458691,0.0004294192,0.0007799831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004510902,"about_ca_system_score_gemma":0.0009055053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001438293,"about_ca_topic_score_gemma":0.002739613,"domain_scores_codex":[0.9995895,0.00005188497,0.00003361034,0.00009547184,0.0001880052,0.00004148827],"domain_scores_gemma":[0.9992641,0.0002085783,0.0001713396,0.0001605639,0.0001721689,0.00002336473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004868069,0.0000722869,0.004786581,0.0002967408,0.00004691728,0.0001441364,0.000382753,0.025788,0.3936894,0.01504223,0.001788034,0.5574761],"study_design_scores_gemma":[0.00009608018,0.0009963526,0.01835749,0.00005804661,0.0002132073,0.002400812,0.0002534146,0.2424355,0.6858845,0.0119042,0.03722247,0.0001778226],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06322768,0.0003658181,0.931912,0.0001069647,0.0001018422,0.0000554263,0.0000364769,0.001458078,0.002735703],"genre_scores_gemma":[0.4129428,0.0004502681,0.5833778,0.00004913198,0.00005710596,0.00006069316,0.0001031111,0.0002360303,0.002723052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001801335,"threshold_uncertainty_score":0.006026089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00989255395078391,"score_gpt":0.3492175785285807,"score_spread":0.3393250245777967,"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."}}