{"id":"W6930266120","doi":"10.5281/zenodo.12934895","title":"strategic compensation in canada 7th edition free","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Compensation (psychology); Government (linguistics); Compensation of employees; Work (physics); Executive compensation; Strategic planning","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004665625,0.0001888909,0.0001947129,0.0003937825,0.0002263576,0.000716312,0.001790692,0.0001094826,0.006091769],"category_scores_gemma":[0.00007251166,0.0001965398,0.00003453569,0.0006143418,0.00004023187,0.0001391167,0.001052589,0.0004115721,0.002527674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004680547,"about_ca_system_score_gemma":0.000052878,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06954513,"about_ca_topic_score_gemma":0.01615738,"domain_scores_codex":[0.9981124,0.0003532249,0.0002275441,0.0005681635,0.0004272004,0.0003113988],"domain_scores_gemma":[0.9988781,0.00001310697,0.0001127765,0.0007598525,0.0001131121,0.0001230932],"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":[0.000002154553,0.00001729454,8.56872e-8,0.00008821568,0.00001491433,0.00004507749,0.0001180575,0.000005922765,0.00003968797,0.08173531,0.8844684,0.03346489],"study_design_scores_gemma":[0.0002095061,0.00004251809,0.0000158228,0.0001558853,0.000006835145,0.00004185547,0.00003737039,0.00310897,0.00001934111,0.01765663,0.9784818,0.0002234913],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000006373946,0.0002441751,0.3241751,0.0005524904,0.0004264987,0.0002569409,0.000185432,0.0005616625,0.6735914],"genre_scores_gemma":[0.07210868,0.001447956,0.1713347,0.002555903,0.005071931,8.682708e-7,0.009922078,0.04747667,0.6900812],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1528404,"threshold_uncertainty_score":0.998249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03698087540899617,"score_gpt":0.2401870757684611,"score_spread":0.203206200359465,"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."}}