Trialling tools using hand‐weeding, weed mat and artificial shading to control nuisance macrophyte growth at multiple scales in small agricultural waterways
Bibliographic record
Abstract
ABSTRACT Introduced aquatic macrophytes can dominate small agricultural waterways in summer and autumn becoming a significant management problem. Excessive growth can clog waterways, causing drainage issues and reducing agricultural productivity while in‐stream velocities are reduced and sedimentation increased. Consequently, water managers remove them by mechanical clearance, chemical spray and cutting which can be costly and have negative impacts on in‐stream habitat and ecological health. We trialled three tools to reduce macrophytes: hand‐weeding, weed mat and artificial shading, at a reach‐scale (50 m) and larger‐scale (200–400 m). Hand‐weeding reduced cover in the short‐term, however macrophytes recovered to pre‐treatment levels within one season. Weed mat along the banks was effective at reducing emergent macrophytes, particularly Erythranthe guttata (monkey musk) and Nasturtium microphyllum (watercress). Weed mat lasted for several growing seasons and continued to be effective. Shading over the waterway using polythene markedly reduced submerged macrophytes indicating that heavy shading by riparian plantings could reduce submerged macrophytes in the longer‐term. These results indicate that in the short‐term, weed mat could be used to limit sprawling emergent macrophytes. In the longer‐term, weed mat used in conjunction with riparian planting could provide shading so that macrophyte cover in these small waterways could be reduced and controlled.
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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) | 0.001 | 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 | 0.001 | 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".