{"id":"W6889076333","doi":"10.25545/bhmcou","title":"Crack Roboflow DTI UNB","year":2024,"lang":"en","type":"dataset","venue":"UNB Dataverse","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Department of Transportation, Infrastructure and Energy; Current Water Technologies (Canada); University of New Brunswick","funders":"","keywords":"Annotation; Segmentation; Image segmentation; Visualization; Resource (disambiguation); Pattern recognition (psychology)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009246177,0.005312755,0.00243223,0.005591842,0.002205646,0.002838675,0.00587812,0.005037927,0.05197106],"category_scores_gemma":[0.004216341,0.001065918,0.001943369,0.005640312,0.001147755,0.002609711,0.003520408,0.003040618,0.08435023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002569198,"about_ca_system_score_gemma":0.002354932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05952337,"about_ca_topic_score_gemma":0.1556626,"domain_scores_codex":[0.9982417,0.0001716495,0.0001145352,0.0006407001,0.0005357517,0.0002957807],"domain_scores_gemma":[0.9986772,0.0002177263,0.00009873528,0.0004648274,0.0003958087,0.0001457024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001500416,0.00009785534,0.000764977,0.0009308247,0.00005542051,0.0001169052,0.00007118395,0.0006966158,0.001010177,0.0005984336,0.9825401,0.01296743],"study_design_scores_gemma":[0.0002660515,0.0001002002,0.006707693,0.0005162217,0.00008561264,0.0006798711,0.0002882417,0.006669701,0.004617393,0.002042865,0.9779063,0.0001198066],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002297898,0.0009423941,0.0009333218,0.0001988726,0.0001731209,0.0001400669,0.9833329,0.007081066,0.004900445],"genre_scores_gemma":[0.001513906,0.00009609741,0.001698112,0.00006991742,0.00001212518,0.0001516199,0.9945871,0.0003681915,0.001503097],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9480289,"threshold_uncertainty_score":0.1738605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02265830510564791,"score_gpt":0.2838237375920988,"score_spread":0.2611654324864509,"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."}}