{"id":"W4409795695","doi":"10.61091/jcmcc127b-458","title":"Fine Structural Characterization of Construction Asphalt Mixtures Based on Image Processing Techniques","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Asphalt Pavement Performance Evaluation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Asphalt; Characterization (materials science); Image processing; Image (mathematics); Computer science; Process engineering; Materials science; Artificial intelligence; Engineering; Composite material; Nanotechnology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003067606,0.0006154041,0.0003229952,0.002629929,0.0002060713,0.0006131203,0.0003586836,0.0004949914,0.0007266309],"category_scores_gemma":[0.0005413943,0.0002458245,0.0004970756,0.001337537,0.0003190603,0.001062613,0.0002804398,0.0004085302,0.0002375732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001896972,"about_ca_system_score_gemma":0.0002243886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001081481,"about_ca_topic_score_gemma":0.001386729,"domain_scores_codex":[0.9997829,0.00001696362,0.00001441359,0.00005040232,0.0001086097,0.0000268002],"domain_scores_gemma":[0.9997774,0.00005447131,0.0000527219,0.00002476879,0.00007797077,0.00001280293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002066689,0.000120142,0.009095926,0.0003824224,0.00006011064,0.000303773,0.0003231774,0.02011715,0.7501324,0.001674557,0.0004803191,0.2171033],"study_design_scores_gemma":[0.00002048611,0.0002576354,0.05813959,0.000040142,0.0001985224,0.0007202039,0.0005351641,0.3586783,0.5756673,0.001907322,0.00373337,0.0001019433],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5282506,0.0008234067,0.4660999,0.00009469046,0.00003679208,0.00008685714,0.0002558546,0.0009909838,0.00336088],"genre_scores_gemma":[0.8391833,0.0008402478,0.1581474,0.00005429377,0.00003017186,0.00006368758,0.0003597735,0.00008788954,0.001233158],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002629929,"threshold_uncertainty_score":0.002430856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007049306328088949,"score_gpt":0.2518564319379664,"score_spread":0.2448071256098774,"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."}}