Germination requirements for 29 terrestrial and wetland wild plant species appropriate for phytotoxicity testing
Bibliographic record
Abstract
BACKGROUND: Species selected for phytotoxicity testing have been limited to a few standard crop species owing to restrictive recommendations at the regulatory level. However, guidelines by the Organisation for Economic Development and Cooperation (OECD) were recently amended in 2006 to include a list of herbaceous non-crop plant species suitable for testing. The objective of this study was to outline the optimum germination requirements for a selection of wild species for which seeds were readily available from commercial suppliers. RESULTS: Of the 29 herbaceous terrestrial and wetland species included in this study, all achieved 50% germination and 23 reached > 70% germination to meet the criterion outlined in the OECD guidelines. Most species attained their maximum germination within 14 days or less. Cold stratification of imbibed seeds improved germination for 14 species. Increasing sowing soil depth did not improve seed germination. The variance attained in this experiment between replicates was low, especially for species with > 70% germination (standard error approximately 5%). CONCLUSION: The present study showed that 23 of the 29 species tested required minimal pretreatments and produced consistent, reliable and uniform germination reaching at least 70%. The inclusion of wild plant species in regulatory testing should be given real consideration.
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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".