Seasonal and decadal patterns in <i>Discostella</i> (Bacillariophyceae) species from bi‐weekly records of two boreal lakes (Experimental Lakes Area, Ontario, Canada)
Bibliographic record
Abstract
A recent rise in the relative abundance of Discostella species (D. stelligera and D. pseudostelligera) has been well documented from sedimentary diatom assemblages across the Northern Hemisphere. This unprecedented change over the last ~150 years has been linked to rises in atmospheric temperatures, changes in ice cover, and/or increases in thermal stability, among other factors. The bi-weekly monitoring data from two boreal lakes at the Experimental Lakes Area (ELA) in northwestern Ontario were analyzed across seasons (spring, summer, and fall) and decades (1970s-2000s). We found that Discostella species are primarily spring/early summer bloomers (i.e., late April to June) in these lakes and changes in concentrations of Discostella over time were most pronounced in the spring or early summer months. Increases in Discostella abundance over time may be linked to earlier ice-off and a longer period of spring turnover, resulting from increased winter and spring temperatures. It is also possible that a trophic mismatch between the spring diatom bloom and zooplankton is occurring, thus reducing diatom loss rates, and resulting in greater overall abundance. Moreover, the spring dominance of Discostella in our study lakes occurred at a time of the year when nutrient concentrations were at their highest seasonally, suggesting that these taxa are neither limited directly by nutrients, nor responding to enhanced stratification during the summer months in these lakes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".