{"id":"W6922395646","doi":"10.12755/isec.res.2019.179","title":"THE POTENTIAL OF KNOWLEDGE MANAGEMENT ON CONSTRUCTION SITES","year":2019,"lang":"en","type":"dataset","venue":"NRCT Data Center","topic":"Educational Robotics and Engineering","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Variety (cybernetics); Multidisciplinary approach; Order (exchange); Phase (matter); Personal knowledge management; Quarter (Canadian coin); Investment (military)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001525986,0.0008296714,0.0008146367,0.005421747,0.0007611278,0.002302798,0.001868974,0.001727175,0.009404101],"category_scores_gemma":[0.01214905,0.0003242472,0.001064042,0.01003106,0.0003499652,0.002449709,0.002213348,0.001088401,0.008147659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00193029,"about_ca_system_score_gemma":0.001380673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03656036,"about_ca_topic_score_gemma":0.04196307,"domain_scores_codex":[0.9973327,0.0006982108,0.0003297639,0.0006561843,0.0006396074,0.000343549],"domain_scores_gemma":[0.9949989,0.002371243,0.0005460181,0.0009892598,0.0008451023,0.0002495305],"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":[0.0006230802,0.0002220272,0.0668678,0.004989191,0.0002191144,0.0003207835,0.0005932463,0.008199493,0.0006817009,0.005922681,0.8450428,0.06631817],"study_design_scores_gemma":[0.0001436457,0.00006402571,0.1021855,0.0011652,0.00009009169,0.0002991849,0.001542235,0.005836111,0.0008335775,0.004981914,0.8827792,0.00007934006],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01595933,0.001011861,0.0006553464,0.0007298369,0.00007850108,0.00006315479,0.9744625,0.0003304568,0.006708942],"genre_scores_gemma":[0.01826068,0.0003208424,0.001188508,0.00009774704,0.00001579535,0.0001309155,0.9788784,0.00002826933,0.001078899],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03656036,"threshold_uncertainty_score":0.07269508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02902906653157603,"score_gpt":0.2757443718838415,"score_spread":0.2467153053522655,"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."}}