Harvest Loss and Seed Bank Longevity of Flax (<i>Linum usitatissimum</i>) Implications for Seed-Mediated Gene Flow
Bibliographic record
Abstract
Flax is a minor oilseed crop in Canada largely exported to the European Union for use as a source of industrial oil and feed ingredient. While flax could be genetically engineered (GE) to enhance nutritional value, the adoption of transgenic technologies threatens conventional flax market acceptability. Harvest seed loss of GE crops and the persistence of GE crop volunteers in the seed bank are major factors influencing transgene persistence. Ten commercial fields in Alberta, Canada, were sampled after harvesting conventional flax in 2006 and 2007, and flax seed density and viability were determined. Additionally, artificial seed banks were established at two locations in Alberta in 2005 and 2006 to quantify persistence of five conventional flax cultivars with variability in seed coat color (yellow or brown) and α-linolenic acid (ALA, 18:3cisΔ9,13,15) content (3 to 55%) at three soil depths (0, 3, or 10 cm). Harvest methods influenced seed loss and distribution, > 10-fold more seed was distributed beneath windrows than between them. Direct harvested fields had more uniform seed distribution but generally higher seed losses. The maximum yield loss was 44 kg ha−1or 2.3% of the estimated crop yield. Seed loss and the viability of flax seed were significantly influenced by year, presumably because weather conditions prior to harvest influenced the timing and type of harvest operations. In artificial seed bank studies, seed coat color or ALA content did not influence persistence. Flax seed viability rapidly declined in the year following burial with < 1% remaining midsummer in the year following burial but there were significant differences between years. In three of four locations, there was a trend of longer seed persistence at the deepest burial depth (10 cm). The current study predicts that seed-mediated gene flow may be a significant factor in transgene persistence and a source of adventitious presence.
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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 | 0.000 | 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) | 0.001 | 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 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".