Evaluation and Dynamic Optimization of Big Data Technology in Engineering Project Resource Allocation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper focuses on the innovative application of big data technology in the field of engineering project resource allocation, deeply analyzing its core functional mechanisms in resource evaluation and dynamic optimization. By constructing an evaluation index system for resource allocation that integrates multi-source data, and combining data mining, machine learning, and deep learning algorithms, an intelligent dynamic optimization model for resource allocation is established. Taking a super-high-rise commercial complex construction project as a typical case, this paper details the full-process practice of big data technology from data collection and analysis to optimization decision-making, and quantitatively analyzes its significant effects in improving resource utilization efficiency, reducing project costs, and ensuring construction progress. The study shows that big data technology can provide scientific and precise decision-making basis for engineering project resource allocation, strongly promote the transformation of engineering project management towards intelligence and refinement, and provide new technical paths and practical references for industry development.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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