Establishing field stands of Russian dandelion (<i>Taraxacum Kok-saghyz</i>) from seed in southern Ontario, Canada
Bibliographic record
Abstract
Taraxacum Kok-saghyz (L.E. Rodin) (TKS) is a natural source of rubber. There is a rising interest in TKS establishment as a new crop in Ontario. Trials were conducted at Simcoe and Guelph, Ontario between 2013 and 2014 to explore the possibility of establishing TKS plants from seed in fields in Ontario. The effects of seeding depth, watering regimes, polymer, and covering mulch on the recruitment of TKS were explored. It was found that TKS displays very low field recruitment levels regardless of treatment. KNO3 did not have any significant effect on TKS seed germination. Seedling emergence was greatest when seeds were placed on the soil surface and decreased with increasing seeding depth. Two years of field experiments highlighted that TKS seedlings recruit better in moderate conditions when the temperature is optimum for TKS germination (warm but not hot, <25 °C). TKS seedlings recruited better at Simcoe (with a sandy soil type) than at Guelph (with a loamy soil type). Overall, however, field recruitment from seed was low (15% at best) and these results suggest that establishing TKS from seed in Ontario fields remains a challenge.
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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".