Reduction of queuing delays at waste management facilities
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
ile waiting to unload materials at waste management facilities such as landfill sites, transfer stations, and material recovery facilities. These delays can be costly since the program operator must pay for these trucks and their crews to sit idly. Previous studies of delays at unloading facilities have often focussed on reducing unloading times, primarily through capital improvements such as providing twin scale houses and additional unloading bays. Most of these studies assume that the "arrival pattern of the collection vehicles is beyond the control of the analyst. This work assumes that the physical layout of the unloading facility is fixed and examines the effect that changes in the arrival times of collection vehicles will have on queuing delays at the facility. Both deterministic and fluid flow approaches to the analysis of queuing delays at unloading facilities are presented. The results show that congestion at unloading facilities is often caused by the assignment of approximately equal workloads to each collection crew and that relatively minor differences in workload assignments can substantially reduce queuing delays. The results of the analysis are confirmed through Monte Carlo simulation modelling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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