{"id":"W3118367691","doi":"10.3390/s21020364","title":"Development of Integrative Methodologies for Effective Excavation Progress Monitoring","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Excavator; Excavation; Volume (thermodynamics); Automation; Lidar; Computer science; Engineering; Remote sensing; Civil engineering; Geotechnical engineering; Geology; 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":[0.001989982,0.001204354,0.0008577944,0.002890351,0.0003878667,0.001807822,0.002033714,0.0008788296,0.001565791],"category_scores_gemma":[0.005552031,0.0006445018,0.0008949771,0.001932635,0.0006737566,0.002922776,0.002160751,0.0007699237,0.0006966894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006565227,"about_ca_system_score_gemma":0.001178738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001768641,"about_ca_topic_score_gemma":0.00241064,"domain_scores_codex":[0.9982735,0.00033423,0.0001628751,0.0005293978,0.0006132201,0.00008672073],"domain_scores_gemma":[0.9976748,0.0005532309,0.0004490116,0.0003771927,0.0008850315,0.00006091663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005726071,0.0001123326,0.004128919,0.0006651515,0.0001919636,0.0001737468,0.0004182734,0.1023959,0.03256895,0.0261138,0.001937521,0.8312362],"study_design_scores_gemma":[0.00001371967,0.0001386765,0.003911762,0.0001818559,0.0001163767,0.0003822341,0.0004413812,0.9142851,0.02503855,0.03834606,0.01708613,0.0000581827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001645154,0.0002265628,0.9971975,0.00003846552,0.00001351517,0.00003584358,0.00003864454,0.0002912802,0.0005131047],"genre_scores_gemma":[0.09166987,0.0009053675,0.9056462,0.00005294065,0.00005159487,0.0002010901,0.00029148,0.00009278641,0.001088641],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002890351,"threshold_uncertainty_score":0.01052409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02796273450150888,"score_gpt":0.3082845495425588,"score_spread":0.2803218150410499,"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."}}