Co-optimization Research on Digitalization of Enterprise Human Resource Management and Integrated Construction of Measurement and Training Based on Optimization Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we construct a multi-level network based on corporate mobility relationships to quantify human resource attributes.The cuckoo search algorithm (CS) is chosen to enhance the global optimization capability of human resource management scheme.Combine CS and XGBoost to construct CS-XGBoost algorithm, and realize the optimal solution of HRM scheme through hyperparameter optimization and other steps.The multi-project human resource management of construction enterprises is taken as an example to verify the auxiliary value of CS-XGBoost algorithm in the generation of optimal management scheduling scheme.Empirical studies show that the algorithm can obtain the optimal solution in about 450 iterations.In multi-project scheduling management, the optimal duration can be reduced to 510 days, which is better than the comparison algorithm.With the introduction of demand prioritization requirements, the algorithm can effectively balance the differences in project duration, project cost and employee working time.The CS-XGBoost algorithm can be used to quickly realize the optimal decision-making of enterprise human resource scheduling management, save costs and improve efficiency.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it