Modeling the effects of ultraviolet radiation on embryos of <i>Calanus finmarchicus</i> and Atlantic cod (<i>Gadus morhua</i>) in a mixing environment
Bibliographic record
Abstract
It is well established that ultraviolet radiation (UVR, 280–400 nm) harms aquatic organisms. Reductions in productivity have been reported for phytoplankton, ichthyoplankton, and zooplankton in incubations exposed to UVR. It is difficult, however, to estimate the effects of UVR in natural waters. Quantitative assessments of UVR effects on aquatic organisms require high‐resolution measurements of solar irradiance and its attenuation in the water, spectral weighting functions for biological effects, and realistic descriptions of the distributions and vertical movements of particles in the water column. Using experimentally determined biological weighting functions for UV‐induced mortality along with measurement‐based models of solar irradiance and of vertical distributions of embryos as influenced by mixing, we modeled UVR‐induced mortality in the early life stages of two key species in the upper estuary and Gulf of St. Lawrence, Atlantic cod (Gadus morhua) and the planktonic copepod, Calanus finmarchicus. G. morhua embryos are insensitive to UVR, with an average daily survival of ~99% over numerous environmental conditions. C. finmarchicus are considerably more vulnerable, with an average survival of 90% ± 12% (SD). Lowest modeled daily survival was 59% under ambient ozone and 49% under 50% ozone loss. A sensitivity analysis allowed us to examine the relative influences of hydrographic variability, meteorological conditions, and ozone depletion on UVR‐induced mortality in C. finmarchicus embryos. The modeled hydrographic and meteorological conditions are a representative range of natural variability for the St. Lawrence region during the 1997 field season, with the exception of extreme ozone depletion (50%). Effects are expressed as relative change of survival normalized to survival under a reference simulation. Similar to other studies, water column mixing and water clarity have the most significant influence on embryo survivorship, with a 3%–80% increased chance of survival when in static, compared with mixed waters, and a 3%–46% increased chance of survival when in the darkest, compared with the clearest waters. Cloudy skies increase survivorship between 1%–30%, and ozone depletion of 50% can decrease survivorship by 9%. On average, ozone depletion decreases survival by 3% and of the factors considered has the smallest influence on mortality of C. finmarchicus embryos.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".