Detailed scheduling of harvest teams and robust use of harvest and transportation resources
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
Planning activities of harvest teams (harvesting and forwarding) and transportation is critical for efficient procurement of roundwood from forests to mills. The planning process involves many integrated decisions that consider process, spatial and temporal aspects. The spatial aspect concerns which area to harvest, which machine team to use, the mill to which the timber should be allocated and where to store the timber. The process decisions involve which bucking instruction to use. The temporal aspect concerns when to harvest, when to transport in order to meet specific demand at mills, and when to store the timber. Temporal decisions also include determining a detailed schedule for each harvest team. Such a schedule includes starting time and movement time between harvest areas. This is complicated by the harvest team having different home bases and different machine systems with their specific performance description and capacities. The overall planning problem can be formulated into one optimization model, but such a model is too large for practical use and cannot be solved in a reasonable time. We propose a decomposition scheme where a sequence of aggregated models, or limited parts of the model, is solved to find high-quality solutions quickly. We test the scheduling in cases involving two large Swedish forest companies.
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 | 0.002 | 0.001 |
| 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 | 0.000 | 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