Riparian canopy type, management history, and successional stage control fluxes of plant litter to streams
Bibliographic record
Abstract
The food webs of forest stream ecosystems are based primarily on cross-boundary flows of terrestrial plant litter (PL). As such, changes in the composition of riparian forest canopies can alter flows of PL, affecting the ecosystem functioning of adjacent streams. We measured seasonal changes in PL fluxes to 20 small streams flowing through temperate rain forests in southwestern British Columbia to determine how riparian forest type and riparian management history influence resource availability in these systems. Differences in PL fluxes among the four riparian forest types studied were most pronounced in the summer, when PL fluxes from clearcuts < riparian reserve strips and coniferous forests, and PL fluxes from deciduous-dominated forests < riparian reserve strips. PL fluxes also varied among seasons (spring < summer < autumn). Fluxes were similar in riparian reserve strips and coniferous forests of similar age, indicating that reserve strips maintain this crucial ecosystem process. Clearcutting of riparian forests dramatically increased light availability while reducing leaf litter inputs; these effects, however, lasted only a few years. As riparian forest stand age increased, inputs shifted from broadleaf to coniferous inputs, indicating a possible long-term decrease in the lability of the leaf litter resources available to stream consumers.
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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 | 0.001 |
| 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 | 0.000 |
| 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".