{"id":"W4312127315","doi":"10.3390/drones6120407","title":"Texture Analysis to Enhance Drone-Based Multi-Modal Inspection of Structures","year":2022,"lang":"en","type":"article","venue":"Drones","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Segmentation; Modal; Process (computing); Computer science; Visibility; Artificial intelligence; Piping; Pipeline (software); Feature (linguistics); Computer vision; Drone; Visual inspection; Pipeline transport; Pattern recognition (psychology); Engineering; Mechanical engineering","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":[0.0002891468,0.0003982765,0.0002723449,0.001560409,0.0001134524,0.0006884581,0.0002979164,0.0004258732,0.001592454],"category_scores_gemma":[0.0009993508,0.0002014645,0.0003601213,0.0005221815,0.0002382315,0.0006554505,0.000461523,0.0003855138,0.0004925503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000222481,"about_ca_system_score_gemma":0.000166054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001187996,"about_ca_topic_score_gemma":0.00258344,"domain_scores_codex":[0.9997399,0.0000264062,0.000009376914,0.0000552282,0.0001319806,0.00003710984],"domain_scores_gemma":[0.9994804,0.0001462053,0.00007622311,0.00006881831,0.0001984431,0.00002985273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004741148,0.0001060903,0.004981335,0.0003298959,0.00004409709,0.0002047548,0.0002456456,0.02030178,0.6445976,0.001054442,0.001054891,0.3266054],"study_design_scores_gemma":[0.00004345274,0.0004176052,0.04577119,0.00005610052,0.0001333554,0.001323381,0.0002834585,0.620437,0.3205342,0.00187047,0.009051762,0.00007809403],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3094585,0.0005727628,0.6822949,0.0001677323,0.00005277018,0.00007663364,0.0003077946,0.001960216,0.005108644],"genre_scores_gemma":[0.7810034,0.0004075751,0.215602,0.00008357011,0.00003908464,0.00003341703,0.0003810524,0.0002211983,0.002228694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001592454,"threshold_uncertainty_score":0.005327284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004461149719520462,"score_gpt":0.241826391542421,"score_spread":0.2373652418229006,"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."}}