AgBioForum, 16(1): 37-45. ©2013 AgBioForum. Developing a Policy for Low-Level Presence (LLP): A Canadian Case Study
Bibliographic record
Abstract
Agricultural biotechnology research and adoption is increasing. It is estimated that by 2015 there will be a three- to four-fold increase in the number of commercialized biotech products. Also increasing are the complications with international trade given the wide range of acceptance and regulatory capabilities currently in practice globally, specifically, the increasing lowlevel presence (LLP) of biotech products that have received full regulatory approval in one or more countries but not in the country of import. Canada, recognizing the impact of LLP on international trade, is taking a leadership role. Using a government-industry collaborative model, the Canadian government is developing a domestic regulatory policy to manage LLP from imports and building international collaborations to raise awareness of the impacts of LLP on trade globally. This article details the collaborative government-industry process and the current status of the draft domestic LLP policy and international engagement. Key words: agriculture biotechnology, Canada, international trade, low-level presence (LLP), stakeholder collaboration, genetically modified, policy.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.005 |
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".