Does red alder (<i>Alnus rubra</i>) in upland riparian forests elevate macroinvertebrate and detritus export from headwater streams to downstream habitats in southeastern Alaska?
Bibliographic record
Abstract
We assessed the influence of riparian forest canopy type on macroinvertebrate and detritus export from headwater streams to downstream habitats in the Tongass National Forest, southeastern Alaska. Twenty-four fishless headwater streams were sampled monthly, from April to August 1998, across four riparian canopy types: old growth, clearcut, young-growth alder, and young-growth conifer. Young-growth alder sites exported significantly greater count (mean = 9.4 individuals·m3water, standard error (SE) = 3.7) and biomass (mean = 3.1 mg dry mass·m3water, SE = 1.2) densities of macroinvertebrates than did young-growth conifer sites (mean = 2.7 individuals·m3water, SE = 0.4, and mean = 1.0 mg dry mass·m3water, SE = 0.2), enough prey to support up to four times more fish biomass if downstream habitat is suitable. We detected no significant differences in macroinvertebrate export between other canopy types or in detritus export among different canopy types. Roughly 70% of the invertebrates were aquatic; the rest were terrestrial or could not be identified. Although we do not recommend clearcutting as a means of generating red alder, maintaining an alder component in previously harvested stands may offset other potentially negative effects of timber harvest (such as sedimentation and loss of coarse woody debris) on downstream, salmonid-bearing food webs.
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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 | 0.000 | 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".