Influence of Soil Moisture Stress on Vegetative Growth and Mycorrhizal Colonization in Hound’s-tongue (<i>Cynoglossum officinale</i>)
Bibliographic record
Abstract
Hound’s-tongue is an invasive, biennial weed that thrives in dry rangelands of British Columbia. Rosette formation in the first year of growth and a deep root system offer this weed a competitive advantage against associated grasses under dry conditions. To study effects of water stress on seedling growth and mycorrhizal colonization in hound’s-tongue, seedlings of this weed were grown in pots in a greenhouse and subjected to four (100, 80, 60, and 40% of field capacity) soil moisture treatments. Effects of soil moisture stress (SMS) on several growth parameters as well as mycorrhizal colonization of roots were studied. The total biomass, shoot and root fresh and dry weights, leaf number, petiole length, leaf area, and specific leaf weight (leaf dry weight per unit leaf area) decreased with increasing SMS; shoot and root water content was not affected. Because of a greater effect of SMS on root compared with the shoot biomass, shoot:root ratio increased as the moisture stress increased. Water stress decreased mycorrhizal colonization and arbuscule and vesicle abundance. A reduction in total biomass, leaf number and leaf area per plant, petiole length, and mycorrhizal colonization may reduce the competitive advantage of hound’s-tongue over its neighbors under drought conditions. The effect on plant size may also influence herbivory, by biocontrol agents and other herbivores, and fecundity of this weed.
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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".