Time-based combinatorial auction for timber allocation and delivery coordination
Bibliographic record
Abstract
The timber auction system currently used in the province of Québec, Canada, is a single unit auction, in which timber users bid on the entire forest stands located within a specific area. In this procurement system, timber users (i.e., winners) are responsible for harvesting the entire stands and for reselling undesirable timber species to others. In order to improve the limits of this system, this paper proposes a sustainable auction system, referred to as time-based timber combinatorial auction. In this approach, time is not part of the definition of the goods for sale. It is used to valuate the good for sale with respect to their expected delivery period. Therefore, this system aims to simultaneously allocate multiple goods, or products in mixed forest stand, to multiple winners, and address the coordination of timber deliveries to their winners. The proposed timber combinatorial auction provides an open access allocation of timber, based on its intrinsic economic value, while allowing the Ministry of natural resources to exercise high standard for environmentally friendly forest operations. From a logistic point of view, a sensitive analysis is conducted in order to compare the proposed time-based combinatorial auction with a combinatorial auction with no delivery coordination. Both models are compared according to bidders' and seller's time flexibility. Experimental results illustrate the impact (i.e., cost) of delivery coordination on total revenue due to loss of value when time preference is not fully satisfied. This cost evaluation can then be used as an upper bound of the cost of coordination, when delivery coordination must be manually negotiated among multi-stakeholders.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".