Bibliographic record
Abstract
In North America for many years the commonly held solution to white pine blister rust ( Cronartium ribicola J.C. Fischer) (WPBR) was to eradicate all currants and gooseberries ( Ribes L.). That approach was tried to no avail. Can currants and gooseberries be successfully grown in North America? You bet they can! Vast areas of the United States and Canada are ideal for Ribes production. Black currants ( Ribes nigrum L.) are a processed fruit and production may compare to that of grain. Many of the areas that presently grow other berries could easily grow Ribes . The main barriers for production in North America are state restrictions and the availability of up-to-date information and data for growers, processors, legislators and the consuming public. I suggest that this conference and the people herein form that task group and initiate the cooperative dialogue and set forth a process to approach the WPBR problem in a holistic manner.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.062 | 0.017 |
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".