{"id":"W7033203786","doi":"","title":"Predictive Worker Resource Characterization at the Extreme Edge","year":2025,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Testbed; Benchmark (surveying); Task (project management); Resource allocation; Resource (disambiguation); Enhanced Data Rates for GSM Evolution; Computational resource; Throughput","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00002208608,0.0002608378,0.0001672205,0.00009081695,0.0003800246,0.00003504765,0.0004366275,0.0003941755,0.0001123724],"category_scores_gemma":[0.000009437385,0.0002532696,0.0001352441,0.000294533,0.00006194707,0.00002478089,0.0002237484,0.0002694868,0.00001072222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006877445,"about_ca_system_score_gemma":0.0001882288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005671703,"about_ca_topic_score_gemma":0.00004357352,"domain_scores_codex":[0.9989318,0.00007223849,0.0001194393,0.000524897,0.0001037303,0.0002478822],"domain_scores_gemma":[0.9991309,0.00001588401,0.0001960439,0.0005417051,0.00005290087,0.00006256123],"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":[0.001343979,0.0001188467,0.0007637463,0.00009007032,0.0002167891,0.000008362929,0.0002216341,0.00004076964,0.1456189,0.001304222,0.8464646,0.003808015],"study_design_scores_gemma":[0.0001123229,0.00005802858,0.00134793,0.0000533255,0.00005387582,1.417879e-7,0.0001998706,0.000001802009,0.2162023,0.00003934774,0.7817335,0.0001976356],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.6525275,0.001228751,0.06386021,0.06919023,0.000783627,0.006083273,0.002833966,0.001286831,0.2022056],"genre_scores_gemma":[0.01370863,0.001669066,0.0006182045,0.0003010123,0.0001443614,0.00001650056,0.02299567,0.00005096009,0.9604956],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.75829,"threshold_uncertainty_score":0.999992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004571371345987935,"score_gpt":0.2157961033299077,"score_spread":0.2112247319839198,"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."}}