Coupled simulation of high‐frequency dynamics of dissolved oxygen and chlorophyll widens the scope of lake metabolism studies
Bibliographic record
Abstract
Abstract High frequency time series of dissolved oxygen (DO), delayed chlorophyll fluorescence (Chl) and relevant background variables were recorded on nearly 900 d during 7 yr in large, shallow, meso‐eutrophic Lake Balaton (Hungary). Novel models were developed for coupled simulation of diel dynamics of DO and Chl using sequential learning and uncertainty assessment in a Bayesian framework. Despite the generally good model fit for both variables, the uncertainty of the metabolic estimates was high, due primarily to the identification problem of individual metabolic processes. Deviations between observed and simulated DO concentrations suggested that neglect of transient stratification might be responsible for the bulk of the systematic model errors. Net ecosystem production (NEP) was uncertain. Unless air‐water oxygen exchange can be estimated from direct measurements, the free‐water DO method cannot reliably estimate NEP. Gross primary production (GPP) could satisfactorily be hindcasted assuming non‐linear multiplicative dependence on Chl, water temperature and light. Hindcast of community respiration (CR) was less successful, possibly due to the impact of local benthic respiration. Results suggested a major shift in lake metabolism at about 16°C. Below and above this temperature, 70% and 90% of net primary production could be utilized by heterotrophs within a day, respectively. Indirect evidence suggested that biomass‐specific net primary production was determined by phosphorus. The large difference between reproductive rates and net growth rates estimated from GPP and Chl and from daily change in Chl, respectively indicated that loss rates of phytoplankton were as important determinants of algal dynamics as reproductive rates.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".