Bibliographic record
Abstract
Recently selected as a winner of the government of Canada’s COVID-19: Digital clearinghouse challenge, our background work has uncovered that the cost of distribution can often be significantly higher than the cost of manufacture for high consumable medical supplies, like personal protective equipment (PPE). What’s worse, all of these costs are often not realized in suppliers’ pricing schedules, as further ‘hidden costs’ are incurred when governments procure centrally but use locally, demanding after the fact ‘sub distribution’. As the public and private sector alike look to rebuild stockpiles, how can we rethink the supply chain to maintain domestic production without simple subsidization? Conventionally, domestic suppliers have been unable to compete with overseas counterparts on price point. If distribution costs can be lowered, domestic supplies could become cheaper overall, more ethical and more sustainable. The key is in circumventing the architecture of a supply chain altogether — which is only as strong as its weakest link — and enabling an adaptive net that can match suppliers and distributors to orderers, enabling centralized procurement and direct, shortest path distribution at the same time. This strategy can improve the reliability, efficiency and resiliency of supply chains with impact on health costs.
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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".