{"id":"W4414015778","doi":"10.11159/icbes25.138","title":"Cigarette Smoke-Induced Cell Morphology via Keyence and Zeiss Microscopy","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Diffusion and Search Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of California, San Diego","keywords":"Morphology (biology); Microscopy; Materials science; Cigarette smoke; Optics; Physics; Medicine; Biology; Zoology; Environmental health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002217708,0.0001121726,0.0001467495,0.000130289,0.0001241995,0.0001057996,0.0002631625,0.00004900392,1.297347e-7],"category_scores_gemma":[0.00002640662,0.00008186021,0.00002047009,0.0003553364,0.0001532699,0.000008637373,0.000241667,0.0001283171,5.459235e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000889214,"about_ca_system_score_gemma":0.00002617664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001098956,"about_ca_topic_score_gemma":4.665131e-7,"domain_scores_codex":[0.9991923,0.000004767282,0.0001357199,0.0003221513,0.0001253471,0.0002197425],"domain_scores_gemma":[0.9996675,0.00001975174,0.00004809739,0.00008945452,0.00009555367,0.00007960108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002860005,0.00003075815,0.002675769,0.0001202863,0.00001382076,6.484795e-7,0.00002136117,0.0001039915,0.9856483,0.00764778,0.0003213905,0.003387277],"study_design_scores_gemma":[0.0006908763,0.0004839867,0.01487925,0.0002990461,0.00002019332,0.00004719574,0.00001399741,0.6361166,0.3442603,0.00006706351,0.002796367,0.0003251898],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978058,0.0004812438,0.0007194314,0.0001585435,0.0004357672,0.000123383,8.303263e-7,0.00001014724,0.0002648636],"genre_scores_gemma":[0.9985967,0.00007390383,0.0001504842,0.0001441469,0.00003629921,0.000006513528,1.921216e-7,0.000004625707,0.0009871015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6413881,"threshold_uncertainty_score":0.3338163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004793046929032626,"score_gpt":0.225246562735362,"score_spread":0.2204535158063294,"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."}}