In-stream uptake dampens effects of major forest disturbance on watershed nitrogen export
Bibliographic record
Abstract
Between January 4 and 10, 1998, a severe ice storm impacted large areas of northern New York, New England, and eastern Canada. This storm struck the Hubbard Brook Experimental Forest in New Hampshire on January 7-8, 1998, and caused extensive forest crown damage (>30%) in a narrow elevation band (600-740 m) across the south-facing experimental watersheds. Stream water has been collected and chemically analyzed since 1963 in six experimental watersheds at Hubbard Brook Experimental Forest; thus, we were able to examine the effect of this severe natural disturbance on watershed nutrient export and changes in instream nitrate (NO3-) processing. The ice storm caused large increases in watershed export of NO3- for 2 years after the disturbance, but our examination of in-stream processing suggests that NO3- losses would have been much more dramatic had there not been an increase in in-stream, nitrogen-processing efficiency after the ice storm. The canopy damage that resulted from the ice storm led to increased light availability and large inputs of woody debris to the stream. We suspect that increases in algal production and storage and processing of terrestrial litter account for the increase in inorganic nitrogen processing in these streams. Our results indicate that, without in-stream processing, export of NO3- from the damaged watersheds would have been 80-140% higher than was observed. These results point to an intriguing negative feedback mechanism whereby the same disturbance that causes watershed NO3- loss may simultaneously lead to increased in-stream retention and transformation.
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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) | 0.002 | 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".