{"id":"W4400282938","doi":"10.32473/flairs.37.1.135537","title":"Fluid Path Detection Model for Lab on a Chip Images Using Deep Learning-Based Segmentation Approach","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Artificial intelligence; Computer science; Path (computing); Chip; Deep learning; Lab-on-a-chip; Computer vision; Pattern recognition (psychology); Machine learning; Materials science; Nanotechnology; Microfluidics; Telecommunications","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":[0.0003240692,0.0008117178,0.0004668714,0.0008688598,0.0002520483,0.0007186285,0.001189311,0.001155723,0.001655478],"category_scores_gemma":[0.0007152928,0.0004255208,0.0007277018,0.0004646832,0.0003948456,0.0006643109,0.0004730451,0.0008127635,0.0005519723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001486247,"about_ca_system_score_gemma":0.001282844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01024635,"about_ca_topic_score_gemma":0.01031452,"domain_scores_codex":[0.9998547,0.00001573837,0.000006297063,0.00005670547,0.00003954163,0.00002691349],"domain_scores_gemma":[0.9998429,0.00005298454,0.00002391476,0.00001408985,0.00005516192,0.00001086456],"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":[0.0001919711,0.00009974688,0.001590802,0.00008597234,0.00005003158,0.0000977649,0.00006378287,0.7532337,0.02557009,0.004537953,0.003260256,0.211218],"study_design_scores_gemma":[0.000001278954,0.000007798226,0.00009546429,0.000001885404,0.000002676441,0.000007881959,0.000001771663,0.9972796,0.001710814,0.0006031435,0.000285516,0.00000229784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02543519,0.0002992462,0.9704261,0.000272319,0.00003795925,0.00005849663,0.0002127792,0.00192094,0.001336866],"genre_scores_gemma":[0.5993194,0.0006537053,0.3870082,0.00050638,0.00006533615,0.0003640959,0.001269807,0.0003399976,0.01047313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01024635,"threshold_uncertainty_score":0.0203734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.118825085077113,"score_gpt":0.370022375763588,"score_spread":0.251197290686475,"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."}}