Absence of winter and spring monsoon changes water level and rapidly shifts metabolism in a subtropical lake
Bibliographic record
Abstract
We investigated how the lack of usual winter and spring monsoons, effectively representing consecutive drought events, affected the dynamics of ecosystem metabolism in a shallow mesotrophic seepage lake in northeastern Taiwan. An instrumented buoy provided high-frequency free-water dissolved oxygen measurements, water temperature profiles, and meteorological data, which we used to estimate daily values of gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP). Results revealed that the disappearance of monsoons decreased lake level and volume, concentrated dissolved nutrients, stimulated the development of algal biomass, promoted stratification, and resulted in a major shift in lake metabolism. Offshore GPP and R were both initially stimulated but then decreased due to shallower mixing depths in the water column. The lake rapidly shifted from a heterotrophic state to a highly autotrophic status when the water level dropped to the lowest level. A return to autotrophy was caused by a greater decline in R than an increase in GPP. This study demonstrates the dramatic effect that drought events can have on lake ecosystem function and suggests that nutrient control may be important in mitigating the effects of a predicted warmer and drier climate and increased water withdrawal in this region.
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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) | 0.000 | 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".