The effect of ultraviolet B on phytoplankton populations in clear and brown temperate Canadian lakes
Bibliographic record
Abstract
Beaverskin and Pebbleloggitch lakes are located 2 km apart in Kejimkujik National Park (KNP) and share many limnetic characteristics: pH 5.0, shallow depth, and low nutrient concentrations. The lakes differ mainly in DOC concentrations, as Pebbleloggitch has 13 mg L−1 and Beaverskin 3.5 mg L−1. Consequently, 95% ultraviolet B (UV‐B) extinction occurs in relatively clear water at 50 cm, whereas in brown water it occurs at 4 cm below surface. Two treatments (UV‐B excluded with Mylar and UV‐B exposed) were used with experimental enclosures in the two lakes. In each lake, nine replicates of both treatments were sampled every 2 weeks from 2 July to 14 August 1996. Beaverskin lake phytoplankton differed fundamentally from that of the brown‐water Pebbleloggitch. The community in Beaverskin was relatively simple, consisting of few taxa, mainly cyanobacteria, dominated by Merismopedia tenuissima. Pebbleloggitch, in contrast, hosted many taxa, from all major algal divisions, e.g., Chlamydomonas angulosa, Mougeotia spp., Cryptomonas czosnowskii, Spherocystis sp., and Tabellaria quadriseptata. Phytoplankton species composition and cell densities in Beaverskin Lake did not differ significantly between the exposed and Mylar covered enclosures, except in the final week of collection. In contrast, phytoplankton populations in covered enclosures in Pebbleloggitch Lake were sharply different from those that were not covered. We attribute differences in response to the influence of the brown waters of Pebbleloggitch where the higher rates of absorbance of light by the brown waters results in slower mixing and greater damage by UV‐B radiation. This conclusion is in contrast to the generally accepted view that brown waters provide protection for phytoplankton.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".