Establishment of reed canarygrass (<i>Phalaris arundinacea</i>) in the context of floodplain restoration: impact of companion plant and sowing rate
Bibliographic record
Abstract
Habitat loss and degradation is a leading cause of the current biodiversity crisis. In the lake Saint-Pierre floodplain, agricultural intensification has led to the loss of substantial spawning and rearing areas for the yellow perch ( Perca flavescens Mitchill). Restoring perennial vegetation cover is key to ensure the persistence of the population, but the floodplain conditions limit our ability to do so. In this study, we tested the impact of companion plants ( Avena sativa L., Lolium multiflorum L.) and sowing rate on the establishment success of reed canarygrass ( Phalaris arundinacea L.; RCG) in year 2. RCG tolerates a wide range of environmental conditions and can provide the plant cover essential to the reproduction of yellow perch. We hypothesized that companion plants would reduce weed pressure and in turn improve RCG establishment, and that increasing the sowing rate would improve the establishment success. Contrary to our expectations, using companion plants generally reduced the cover and biomass of RCG. It also led to increased weed prevalence in most treatments. In addition, sowing at high rates did not impact RCG cover and biomass. Sowing RCG alone appears to be the most effective option to establish perennial vegetation supporting the recovery of the yellow perch population.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".