Improvement of freight consolidation through a data mining-based methodology
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
Freight consolidation is a complex logistics practice supported by a broad spectrum of strategies and methods to improve supply chain cost-effectiveness. It consists of grouping products in a single batch to reduce distribution costs. Literature review revealed that operational research (OR) is typically used for freight consolidation, and their inputs are often aggregated over time. While necessary to accommodate computationally expensive OR algorithms, such data simplifications are responsible for losing valuable data patterns. Our contribution is a novel data mining methodology that uses association rules to leverage data patterns in the context of intermittent demand. Our approach is compared to a typical operational research approach from a literature case study. Simple to implement, our methodology gives good results and can flexibly accommodate and exploit data patterns while being able to scale to a much larger amount of data, making it a more suitable approach for the big data world.
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 | 0.000 |
| 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 | 0.001 |
| 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