Light attenuation characteristics of glacially‐fed lakes
Bibliographic record
Abstract
Abstract Transparency is a fundamental characteristic of aquatic ecosystems and is highly responsive to changes in climate and land use. The transparency of glacially‐fed lakes may be a particularly sensitive sentinel characteristic of these changes. However, little is known about the relative contributions of glacial flour versus other factors affecting light attenuation in these lakes. We sampled 18 glacially‐fed lakes in Chile, New Zealand, and the U.S. and Canadian Rocky Mountains to characterize how dissolved absorption, algal biomass (approximated by chlorophyll a), water, and glacial flour contributed to attenuation of ultraviolet radiation (UVR) and photosynthetically active radiation (PAR, 400–700 nm). Variation in attenuation across lakes was related to turbidity, which we used as a proxy for the concentration of glacial flour. Turbidity‐specific diffuse attenuation coefficients increased with decreasing wavelength and distance from glaciers. Regional differences in turbidity‐specific diffuse attenuation coefficients were observed in short UVR wavelengths (305 and 320 nm) but not at longer UVR wavelengths (380 nm) or PAR. Dissolved absorption coefficients, which are closely correlated with diffuse attenuation coefficients in most non‐glacially‐fed lakes, represented only about one quarter of diffuse attenuation coefficients in study lakes here, whereas glacial flour contributed about two thirds across UVR and PAR. Understanding the optical characteristics of substances that regulate light attenuation in glacially‐fed lakes will help elucidate the signals that these systems provide of broader environmental changes and forecast the effects of climate change on these aquatic ecosystems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".