{"id":"W1893695878","doi":"10.24908/pceea.v0i0.4807","title":"Enhanced remote laboratory work for engineering training","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Experimental Learning in Engineering","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cégep de Sherbrooke; Cégep de Sorel-Tracy; Collège de Valleyfield; École de Technologie Supérieure","funders":"École de technologie supérieure","keywords":"Contextualization; Work (physics); Set (abstract data type); Meaning (existential); Engineering management; Virtual Laboratory; Remote laboratory; Computer science; Information and Communications Technology; Space (punctuation); Engineering; Multimedia; The Internet; World Wide Web; Psychology; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002707201,0.000273297,0.0002555422,0.0003325124,0.0001193643,0.000145589,0.0003671022,0.0002240337,0.00003779447],"category_scores_gemma":[0.0009498161,0.0003069664,0.000122105,0.0007538119,0.00001159103,0.0003739872,0.00002060046,0.0003526928,0.0000222647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002375248,"about_ca_system_score_gemma":0.0002223262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006392795,"about_ca_topic_score_gemma":0.0001316122,"domain_scores_codex":[0.9985508,0.000003118367,0.0003828864,0.0002167788,0.0002696473,0.0005768244],"domain_scores_gemma":[0.9988821,0.00009794211,0.000151129,0.0001424611,0.0004548025,0.0002715246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004171289,0.00004586767,0.003970547,0.001345863,0.0004953102,1.127405e-7,0.007697224,0.4091713,0.5047478,0.004589694,0.05709655,0.01083554],"study_design_scores_gemma":[0.001317543,0.00008049384,0.09065797,0.002069893,0.0001842688,0.00000742325,0.002217233,0.3687637,0.2862965,0.0002774322,0.2451803,0.002947314],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759333,0.0008182484,0.004024911,0.001540137,0.007736963,0.002160164,0.00005052027,0.001473382,0.006262338],"genre_scores_gemma":[0.9836035,0.000008123335,0.0150057,0.00009227366,0.0003009968,0.0002306389,0.000008087073,0.0001377074,0.0006129874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2184514,"threshold_uncertainty_score":0.9999382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004710491316069055,"score_gpt":0.1855493549888866,"score_spread":0.1808388636728175,"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."}}