Attenuation of photosynthetically active radiation and ultraviolet radiation in response to changing dissolved organic carbon in browning lakes: Modeling and parametrization
Bibliographic record
Abstract
We present and evaluate an update to the process‐based lake model MyLake that includes a time‐varying linkage between light attenuation of both photosynthetically active radiation (PAR) and ultraviolet (UV) radiation wavelengths to changes in dissolved organic carbon (DOC). In many parts of northeastern North America and Europe, DOC in lakes has rapidly increased, leading to reduced water transparency and increases in light attenuation. These changes alter the vertical light and heat distribution that affect vertical structuring of temperature and dissolved oxygen. We use this model update to test the responsiveness of PAR and UV attenuation to short‐term fluctuations in DOC and with a test case of long‐term browning at Lake Giles (Pennsylvania). Lake Giles has browned significantly since the late 1980s, and three decades of detailed empirical data have indicated more than a doubling of DOC concentrations, and consequent increases in PAR and UV attenuation, warming surface waters, cooling deep waters, and increasing deepwater oxygen depletion. We found that the model performance improved by 16% and 52% for long‐term trends in PAR and UV attenuation, respectively, when these coefficients respond directly to in‐lake DOC concentrations. Further, long‐term trends in surface water warming, deepwater cooling, and deepwater oxygen depletion in Lake Giles were better captured by the model following this update, and were very rapid due to its high water transparency and low DOC. Hence, incorporating a responsive link between DOC and light attenuation in lake models is key to understanding long‐term lake browning patterns, mechanisms, and ecological consequences.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".