Carbon allocation in <i>Euphorbia esula</i> and neighbours after defoliation
Bibliographic record
Abstract
Weeds increase their dominance in a grazed plant community by avoiding herbivory and (or) by tolerating herbivory more than neighbouring plants. After defoliation, allocating carbon to shoots at the expense of roots may confer tolerance. We determined carbon allocation patterns of undefoliated and recently defoliated (75% clipping level) plants of the invasive leafy spurge (Euphorbia esula L.) growing with alfalfa (Medicago sativa L.), Kentucky bluegrass (Poa pratensis L.), or Idaho fescue (Festuca idahoensis Elmer). Plants were labeled with 13CO2 24 h after clipping to determine allocation patterns; all plants had equal access to the 13CO2. Based on relative distribution of 13C, defoliation did not affect the amount of carbon allocated to roots of E. esula. The amount of carbon allocated to shoots of E. esula was higher when growing with P. pratensis than when growing with the other species. Based on relative enrichment of 13C, defoliation increased sink strength of remaining shoots on defoliated E. esula plants. Conversely, roots of unclipped E. esula plants were stronger sinks for carbon than roots of clipped plants. Even though defoliation increased "sink strength" of remaining shoots of E. esula, the amount of carbon allocated to the root system was unaffected by defoliation, suggesting that uninterrupted allocation of carbon to its extensive root system, not increased allocation to its shoot system, confers grazing tolerance.
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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".