Spectrally weighted transparency in models of water-column photosynthesis and photoinhibition by ultraviolet radiation
Bibliographic record
Abstract
We present a simple method for describing the influence of variable attenuation of spectral irradiance, Kd(lambda), on water-column photosynthesis and its inhibition by ultraviolet radiation (UV). The approach is based on weighted water transparency, a calculation introduced by Vincent et al. 1998 (Ann Glaciol 27:691-696). Key results of a depth- and spectrally-resolved model of photosynthesis can be reproduced for a broad range of water types by simple parameterizations using a reference solar irradiance spectrum at the surface and water transparency (1/Kd(lambda)) weighted spectrally for biological effectiveness. Transparency that has been weighted spectrally by the normalized product of irradiance and photosynthetic absorption (PUR-weighted transparency, TPURW) describes spectral effects on photosynthesis in the water column. An empirical parameterization of transparency weighted by the product of surface irradiance and the biological weighting function for inhibition of photosynthesis (TPIRW), along with TPURW, describes the inhibition of water-column photosynthesis relative to the uninhibited rate. Our approach is directly compared with an analysis that used weighted transparency as an indicator of the potential for inhibition of photosynthesis by UV as influenced by variations in chromophoric dissolved organic matter associated with climate change over the past 6000 yr (Pienitz & Vincent 2000, Nature 404:484-487). Results demonstrate how weighted transparency, used as an indicator of potential inhibition, can be transformed into a predictor of biological effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".