Chlorophyll-a growth rates and related environmental variables in global temperate and cold-temperate lakes
Bibliographic record
Abstract
Abstract. Lakes are key ecosystems within the global biogeosphere. However, the bottom-up controls on the biological productivity of lakes, including surface temperature, ice phenology, nutrient loads and mixing regime, are increasingly altered by climate warming and land-use changes. To better understand the environmental drivers of lake productivity, we assembled a dataset on chlorophyll-a concentrations, as well as associated water quality parameters and surface solar irradiance, for temperate and cold-temperate lakes experiencing seasonal ice cover. We developed a method to identify periods of rapid algal growth from in situ chlorophyll-a time series data and applied it to measurements performed between 1964 and 2019 across 357 lakes, predominantly located north of 40°. Long-term trends show that the algal growth windows have been occurring earlier in the year, thus potentially extending the growing season and increasing the annual productivity of northern lakes. The dataset is also used to analyze the relationship between chlorophyll-a growth rates and solar irradiance. Lakes of higher trophic status exhibit a higher sensitivity to solar radiation, especially at moderate irradiance values during spring. The lower sensitivity of chlorophyll-a growth rates to solar irradiance in oligotrophic lakes likely reflects the dominant role of nutrient limitation. Chlorophyll-a growth rates are significantly influenced by light availability in spring but not in summer and fall, consistent with a switch to top-down control of summer and fall algal communities. The growth window dataset can be used to analyze trends in lake productivity across the northern hemisphere or at smaller, regional scales. We present some general trends in the data and encourage other researchers to use the open dataset for their own research questions.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".