Bibliographic record
Abstract
SpeakerThank you for the kind introduction.I want to take a moment and share with you the many products made from the corn refining industry.Increasingly, more and more of the consumer products that enhance our daily lives are made from corn.Our products are ingredients in many of the foods that you eat, whether it is corn oil, cornstarch, or corn sweeteners.Corn is also in the fuel tanks of the cars that you drive in the form of ethanol, and in the pharmaceutical products, paints, glues, and other everyday items that you consume.Another new development produced by one of our member companies is biodegradable plastics from corn that will have an enormous and beneficial impact on the environment.Today, I would like to talk about the subject of Canada and U.S. agricultural trade.My remarks will focus largely on the policy perspective, looking at this issue as it has evolved, and comparing it on a couple of occasions to issues that U.S. agriculture faces with our partners south of the border -Mexico.I think there are some interesting comparisons between all three of the NAFTA partners at certain moments in time.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.109 | 0.033 |
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".