{"id":"W4402475490","doi":"10.1109/ccece59415.2024.10667187","title":"Improving Pavement Crack Segmentation Using Attention Mechanism and Self-gated Activation","year":2024,"lang":"en","type":"article","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Mechanism (biology); Computer science; Segmentation; Materials science; Artificial intelligence","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":[0.0005491153,0.001070586,0.001160044,0.00100824,0.0003994487,0.0009668196,0.002583536,0.001728868,0.00184046],"category_scores_gemma":[0.001311894,0.0005298756,0.001054447,0.0006765536,0.0006822739,0.001614836,0.001194422,0.00129415,0.0005536326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00084049,"about_ca_system_score_gemma":0.001214294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01081666,"about_ca_topic_score_gemma":0.01538406,"domain_scores_codex":[0.9995968,0.00004486916,0.0000131328,0.000189036,0.00007582761,0.00008045645],"domain_scores_gemma":[0.9995939,0.0001776596,0.00003912338,0.00007317733,0.00008607213,0.00003016556],"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":[0.0006901185,0.0005094884,0.005491688,0.0002152269,0.0002222697,0.0002836474,0.0002712994,0.3983554,0.07623652,0.004403334,0.006573039,0.506748],"study_design_scores_gemma":[0.00001133006,0.00004180631,0.0007284486,0.000006336748,0.00002263296,0.00004086709,0.00001239984,0.9924603,0.005067171,0.001145985,0.0004553589,0.000007337072],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1585225,0.001267785,0.8284383,0.0005192586,0.0001281462,0.0001338922,0.0002527404,0.007125341,0.003612118],"genre_scores_gemma":[0.8849152,0.0003550337,0.1099375,0.0003537895,0.00008754233,0.00008624759,0.0005283666,0.0003214321,0.003414768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01081666,"threshold_uncertainty_score":0.02150738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00680184810820583,"score_gpt":0.219711941591903,"score_spread":0.2129100934836971,"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."}}