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
Firms operating in the purchasing of end-of-life vehicles (ELVs) have significant challenges related to the fact that most of the purchased ELVs must be collected efficiently in order to minimize their transportation costs. In this project, we study a reverse logistics problem of a Canadian firm that collects ELVs from a group of dealers and accumulates them at its warehouse for part resale or recycling. This problem can be modeled as a vehicle routing problem (VRP) with different side-constraints. Although prior research has made several contributions to model and solve different variants of the VRP, the specific issue in this project considers solving a VRP with a new combination of constraints, such as customer assignment to the private fleet or an external carrier, time-windows, multi-trip, and loading sequences. We propose a mixed-integer linear programming (MILP) model as well as a heuristic algorithm capable of finding the routes’ planning that minimizes the total transportation costs. The performance of the proposed methods is assessed by generated instances using the data obtained from the company.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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