Spectral light attenuation and the absorption of UV and blue light in natural waters
Bibliographic record
Abstract
The spectral pattern of light attenuation in the ultraviolet (UV) and blue region (360–500 nm) was analyzed for seven high Arctic lakes. The best description of Kd versus wavelength was obtained with an exponential model similar to the model used for absorption by chromophoric dissolved organic compounds (CDOM), but with an additional parameter (Kback) that accounts for background scattering: urn:x-wiley:19395590:media:lno20004530642:lno20004530642-math-0001 where Kd(λ) is the diffuse vertical attenuation coefficient at wavelength λ and S is the exponential slope parameter that characterizes the decrease in attenuation with increasing wavelength. The inclusion of the background parameter gave a significantly better fit and eliminated the systematic deviations over the spectrum that occur in the absence of Kback. The resultant S values (mean = 17.4 µm−1) were on average 47% higher than values calculated without a background parameter, and were more sensitive to between‐lake differences. The same pattern was found when S was estimated from spectral Kd values in three literature data sets, so the new equation will generally increase the estimated values of S based on Kd and provide a more accurate guide to intersystem variability. A compilation of literature data for S (based on Kd and the new equation or on absorbance) showed that its mean value (±SE) is significantly higher in freshwaters (17.1 ± 0.7 µm−1) than in the sea (14.0 ± 0.4 µm−1). The variability in S was highest for low values of Kd or a (340 nm values below 3 m−1). This pattern may be due to instrument‐related problems or more likely represents the real variability in the optical properties of CDOM in low colored systems. The analysis showed that S can vary significantly between and within systems (overall range = 9.2–36.2 µm−1) and that the value obtained also depends on the method of calculation, the wavelength range, and the type of optical measurement.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".