Riparian plant species offer a range of organic resources to stream invertebrate communities through varied leaf breakdown rates
Bibliographic record
Abstract
ABSTRACT Riparian plants provide an important source of energy for freshwater food webs through inputs of leaf litter. Planting riparian buffers with mixed species could enhance the detrital resource supply for invertebrates through varied leaf breakdown rates. To quantify leaf breakdown rates and invertebrate colonisation, we used leaves from eleven grass, shrub and tree species common along agricultural waterways in New Zealand. Breakdown of leaves immersed in a spring‐fed stream differed significantly among species, being fastest for pasture grass ( k = 0.0458 day −1 ) followed by broadleaf, pittosporum, willow, toetoe, poplar, gorse, Carex , eucalyptus, flax, and slowest for cabbage tree leaves ( k = 0.0099 day −1 ). Invertebrate community composition did not differ between leaf species, but consumers were extremely abundant on some leaves (e.g. 51–83 Potamopyrgus snails g −1 pasture grass), indicating coarse detrital resources were in high demand for food or habitat. These breakdown rates could inform selection of riparian plant combinations that will enhance food availability for stream communities, especially continuity of supply, thereby contributing to waterway restoration.
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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 | 0.001 | 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".