Retention, breakdown, and biological utilisation of deciduous tree leaves in an estuarine tidal flat of southwestern British Columbia, Canada
Bibliographic record
Abstract
In an estuarine mudflat connected to a mountainous stream, coarse leaf material from deciduous trees was examined for its utilisation by invertebrates, retention, and breakdown rate. Leaves from the stream were deposited in the upper intertidal parts near the stream mouth. Their standing stocks were high in fall (~23.8 g AFDM·m–2) and mostly disappeared by spring. Breakdown rates of red alder leaf packs were higher at subtidal and lower intertidal stations (k = 0.013–0.027·day–1) than at stream channel and upper intertidal stations (k = 0.006–0.009·day–1). Longer contact of leaves with relatively warmer seawater may have enhanced leaf breakdown by microbial activities. In the upper intertidal stations, the decrease in the amount of deposited leaves from fall to spring (k = 0.023–0.039·day–1) was faster than leaf breakdown in the leaf packs, thus physical export was probably significant in the decrease in deposited leaves. Meanwhile, amphipods dominated the invertebrates colonising leaf packs. The invertebrate densities did not significantly differ between natural and polyester leaf packs, suggesting contribution of leaves as a microhabitat rather than a food source. The carbon and nitrogen stable isotope signatures of amphipods indicated that benthic microalgae were their primary food source regardless of natural or artificial leaf packs.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".