Exploring pest mitigation research and management associated with the global wood packaging supply chain: What and where are the weak links?
Bibliographic record
Abstract
Abstract Global trade continues to increase in volume, speed, geographic scope, diversity of goods, and types of conveyances, which has resulted in a parallel increase in both quantity and types of pathways available for plant pests to move via trade. Wood packaging material (WPM) such as dunnage, pallets, crates, and spools, is an integral part of the global supply chain due to its function in containing, protecting, and supporting the movement of traded commodities. The use of untreated solid wood for WPM introduces the risk of wood boring and wood-infesting organisms into the supply chain, while the handling and storage conditions of treated WPM presents risk of post-treatment contamination by surface-adhering or sheltering pests. The wood-boring and -infesting pest risks intrinsic to the solid wood packaging pathway were addressed in the 2002 adoption and 2009 revision of ISPM 15, which was first implemented in 2005–2006 in North America. Although this global initiative has been widely implemented, some pest movement still occurs due to a combination of factors including; fraud, use of untreated material, insufficient- or incomplete- treatment, and post-treatment contamination. Here we examine the forest-to-recycling production and utilization chain for wood packaging material with respect to the dynamics of wood-infesting and contaminating pest incidence within the environments of the international supply chain and provide opportunities for improvements in pest risk reduction. We detail and discuss each step of the chain, the current systems in place, and regulatory environments. We discuss knowledge gaps, research opportunities and recommendations for improvements for each step. This big picture perspective allows for a full system review of where new or improved pest risk management strategies could be explored to improve our current knowledge and regulations.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".