SOIL MOISTURE IMPACT ON BIOMASS PARTITIONING AND RELATIVE CHLOROPHYLL CONTENT FOR LEGUME GRASS MIXTURES IN A CONTROLLED ENVIRONMENT
Bibliographic record
Abstract
Drought is a widespread abiotic stress that impacts plant growth, productivity and survival.A randomized complete block design pot experiment was conducted to determine the effects of drought on above-ground biomass, root biomass, root/shoot (R/S) and relative leaf chlorophyll content (SPAD) of monoculture and legume-grass mixtures at the Swift Current Research and Development Centre (SCRDC) of Agriculture and Agri-Food Canada (AAFC).The legumes were Canadian milk-vetch (Astragalus canadensis) and white prairie-clover (Dalea candida).The grasses were northern wheatgrass (Elymus lanceolatus) and side-oats grama (Bouteloua curtipendula).Three water treatments (40%, 60% and 80% field capacity) and three cuts were the abiotic factors applied.Except for monoculture side-oats grama, multiple-species forage mixtures were more adaptable than a simple grass-legume mixture or monoculture in a water-limiting environment.The forage mixtures of Canadian milk-vetch and northern wheatgrass tolerated lower moisture levels than the other mixtures.Decreased soil moisture resulted in decreased total biomass and altered biomass allocation to roots resulting in higher R/S ratios in stressed seedlings.The SPAD value of Canadian milk -vetch mixture decreased with water stress, and white prairie-clover+ northern wheatgrass and white prairie-clover+ northern wheatgrass+ Canadian milk-vetch were better adapted to low soil moisture than the monocultures.
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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".