Modeling attenuation of photosynthetically active radiation across the optical gradient in the Laurentian Great Lakes with application to Lake Erie
Bibliographic record
Abstract
Representation of subsurface photosynthetically active radiation (PAR) in biophysical models of the Laurentian Great Lakes (LGL) is imperative to their utility as tools for research and management. Here we consolidated measured vertical profiles of subsurface PAR with concurrent water quality (WQ) data from four LGL. We estimated the diffuse attenuation coefficient of PAR (Kd(PAR)) by fitting an exponential function to measured PAR over depth, and evaluated 68 regressions predicting Kd(PAR) as a function of water quality variables (Kd-WQ regressions). We compare four of the top cross-lake calibrated regressions against two published regressions trained on western Lake Erie (WLE) data. Then, as a case study, we demonstrate the utility of our cross-lake calibrated Kd-WQ regressions with a simplified biophysical model of Lake Erie consisting of the Finite Volume Community Ocean Model with submodules for simulating suspended sediment and dissolved organic carbon (FVCOM-SS-DOC). Twenty-five Kd-WQ regressions were identified as candidates for use in biophysical models based on their skill determined via cross-validation. WLE-trained Kd-WQ regressions were less able to simulate Kd(PAR) and PAR in more transparent waters compared to cross-lake calibrated Kd-WQ regressions, which translated to considerable differences in primary production estimates for the central and eastern basins when using WQ data simulated by FVCOM-SS-DOC. A cross-lake calibrated Kd-WQ regression was installed into FVCOM-SS-DOC, which then simulated spatial patterns of suspended sediments and Kd(PAR). These calibrated Kd-WQ regressions can be used in a variety of biophysical models across optically-distinct waters of the LGL to support adaptive management of nutrient inputs and fisheries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".