Bibliographic record
Abstract
The spring and summer 2010 had been exceptionally wet in Saskatchewan, so wet that farmers were unable to seed certain fields. Yet some fields were covered thick with beautiful canola plants, which germinated from the seeds that had fallen to the ground at last year’s harvest. The weather was good and the price for canola as well. It was tempting to take the combine harvester and harvest this crop, which would be lower yielding than canola carefully seeded at uniform distances and depth, but which had cost nothing in inputs. However no farmer in the neighbourhood of Colonsay dared to take the step. Although the land belonged to them and they had bought the seed the preceding year they were afraid of intellectual property claims. They remembered well the Saskatchewan farmer Percy Schmeiser who lost his case against Monsanto at the Supreme Court. In 1996, one year after transgenic herbicide resistant canola varieties were introduced, he found herbicide resistant canola plants in his field and reseeded the grains of these volunteers. Monsanto persecuted him for patent infringement right up to the Supreme Court. The Supreme Court ruled that the ownership of the patent over the herbicide resistance gene conferred to Monsanto by extension intellectual ownership
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".