Bibliographic record
Abstract
Parameters, such as oil, protein, glucosinolates, chlorophyll content and fatty acid composition, were determined using reference methods for both harvest survey samples and Canadian Canola exports. Canola harvest survey and export data were assessed to evaluate if canola harvest survey data can be extrapolated to predict the quality of the Canadian canola exports. There were some differences in some measured parameters between harvest and export data, while other parameters showed little difference. Protein content and fatty acid composition showed very similar data for harvest and export averages. Canadian export data showed lower oil content when compared to the oil content of harvest survey was mainly due to a diluting effect of dockage in the export cargoes which remained constant over the years (1.7% to 1.9%). Chlorophyll was the least predictable parameter; dockage quality as well as commingling of the other grades in Canola No. 1 Canada affected the chlorophyll content of the exports. Free fatty acids (FFA) were also different for the export and harvest survey. FFA levels are affected by storage conditions; they increase during the shipping season and, therefore, are difficult to predict from their harvest survey averages.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".