Modeling the potential effects of climate change on leaf pack processing in central Appalachian streams
Bibliographic record
Abstract
A computer simulation model was constructed to evaluate some of the projected impacts of climate change, including elevated temperatures and increased frequency and magnitude of floods and droughts, on leaf pack processing in central Appalachian streams. The model simulated microbial processing, invertebrate consumption, and transport along a 1-km second-order stream. We examined the effects of wet and dry years with and without a 2°C temperature increase. Both invertebrates and microbes processed more leaf material under the elevated temperature scenarios; however, the invertebrate response was greater than the microbial response. In the model, microbial processing is represented as a linear function of temperature; a nonlinear response might produce different results. Invertebrates processed a greater percentage of the inputs in wet than in dry years, while microbial processing rates were unaffected. A 20-year flood event occurring in November, January, or March caused more than 50% of the leaf inputs to be exported, leaving little detrital material available for invertebrate consumption. The timing of the flood event made little difference to the simulation results. All climate change scenarios resulted in decreases, sometimes substantial, in coarse particulate organic matter availability to shredders during the summer months.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".