Ecosystem Effects of a Tropical Cyclone on a Network of Lakes in Northeastern North America
Bibliographic record
Abstract
Here we document the regional effects of Tropical Cyclone Irene on thermal structure and ecosystem metabolism in nine lakes and reservoirs in northeastern North America using a network of high-frequency, in situ, automated sensors. Thermal stability declined within hours in all systems following passage of Irene, and the magnitude of change was related to the volume of water falling on the lake and catchment relative to lake volume. Across systems, temperature change predicted the change in primary production, but changes in mixed-layer thickness did not affect metabolism. Instead, respiration became a driver of ecosystem metabolism that was decoupled from in-lake primary production, likely due to addition of terrestrially derived carbon. Regionally, energetic disturbance of thermal structure was shorter-lived than disturbance from inflows of terrestrial materials. Given predicted regional increases in intense rain events with climate change, the magnitude and longevity of ecological impacts of these storms will be greater in systems with large catchments relative to lake volume, particularly when significant material is available for transport from the catchment. This case illustrates the power of automated sensor networks and associated human networks in assessing both system response and the characteristics that mediate physical and ecological responses to extreme events.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".