Widespread variability in overnight patterns of ecosystem respiration linked to gradients in dissolved organic matter, residence time, and productivity in a global set of lakes
Bibliographic record
Abstract
We characterized patterns of nighttime ecosystem respiration (ER) in 23 globally‐distributed lakes using free‐water changes in dissolved oxygen (DO). We considered three alternative models of ER to describe overnight changes in DO: constant ER, linearly declining ER, or logistically declining ER. Variation in ER was widespread. Each model of respiration was found to best fit observed overnight DO dynamics with some degree of frequency in every lake. Constant ER occurred with an average frequency of 62% across lakes and was most commonly the best description of DO dynamics. It never occurred with a frequency < 19% in any lake and ranged up to 90% in one dystrophic lake. The average frequency across lakes with which the linear and logistic models occurred was 21% and 17%, respectively, although they ranged as low as 3% and as high as 42% in some lakes. Although data limitations restricted the majority of our analysis to summer months, annual data records from five lakes suggest there is little seasonal variation in the frequency with which patterns occurred. As our conceptual model predicted, the frequency of constant ER among lakes increased and logistic ER decreased along a gradient of increasing terrestrial influence. However, despite significant correlations with total phosphorus and photosynthetically active radiation, the effect of lake productivity on the frequency of patterns of ER among lakes was less clear. This study suggests that diel variability in ER results from complex interactions between different components of respiration and the forcing factors that govern them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".