{"id":"W4402504364","doi":"10.1021/acsapm.4c01844","title":"Investigating Molecular Interactions between Mucin and Contact Lens Thin Films Using Nuclear Magnetic Resonance","year":2024,"lang":"en","type":"article","venue":"ACS Applied Polymer Materials","topic":"Ocular Surface and Contact Lens","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Canada First Research Excellence Fund; McLean Foundation","keywords":"Mucin; Nuclear magnetic resonance; Contact lens; Materials science; Magnetic resonance imaging; Lens (geology); Chemistry; Optics; Physics; Medicine; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001886695,0.0003837351,0.000191881,0.0001555969,0.0001549915,0.0002172391,0.0002327443,0.0003008647,0.0007165362],"category_scores_gemma":[0.0003246822,0.0001337509,0.0001788843,0.0001247973,0.0002015257,0.0003086104,0.000198812,0.0003552118,0.0001454533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001997272,"about_ca_system_score_gemma":0.000125521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001606933,"about_ca_topic_score_gemma":0.001322411,"domain_scores_codex":[0.9998569,0.00002921109,0.000005740799,0.00002889678,0.00003916578,0.00003996954],"domain_scores_gemma":[0.9997731,0.0001162341,0.0000379899,0.00001077541,0.00003706989,0.00002487682],"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":[0.00001906493,0.000007908132,0.00009670125,0.00002392768,0.000003827571,0.00001509073,0.00001208788,0.00003122291,0.9993039,0.00001156163,0.000003809279,0.0004708505],"study_design_scores_gemma":[0.000003042607,0.0002480446,0.003707306,0.000003850887,0.00001404466,0.00007229058,0.00004685471,0.001587,0.9937512,0.00001860079,0.0005417484,0.000005975627],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935018,0.001680365,0.004009886,0.00004219504,0.00001352769,0.00001895074,0.00004207553,0.00001711697,0.0006739771],"genre_scores_gemma":[0.9913096,0.001496913,0.005754282,0.00007369815,0.00001643317,0.00002707764,0.00009552377,0.00001434468,0.001212053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001606933,"threshold_uncertainty_score":0.003195167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02104087307614311,"score_gpt":0.268867223224591,"score_spread":0.2478263501484479,"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."}}