A comparison of shallow Danish and Canadian lakes and implications of climate change
Bibliographic record
Abstract
Summary 1. Winter temperatures differ markedly on the Canadian prairies compared with Denmark. Between 1 January 1998 and 31 December 2002, average weekly and monthly temperatures did not drop below 0 °C in the vicinity of Silkeborg, Denmark. Over this same time, weekly average temperatures near Calgary, Alberta, Canada, often dropped below −10 °C for 3–5 weeks and the average monthly temperature was below 0 °C for 2–4 months. Accordingly, winter ice conditions in shallow lakes in Canada and Denmark differed considerably. 2. To assess the implications of winter climate for lake biotic structure and function we compared a number of variables that describe the chemistry and biology of shallow Canadian and Danish lakes that had been chosen to have similar morphometries. 3. The Danish lakes had a fourfold higher ratio of chlorophyll‐a: total phosphorus (TP). Zooplankton : phytoplankton carbon was related to TP and fish abundance in Danish lakes but not in Canadian lakes. There was no significant difference in the ratio log total zooplankton biomass : log TP and the Canadian lakes had a significantly higher proportion of cladocerans that were Daphnia. These differences correspond well with the fact that the Danish lakes have more abundant and diverse fish communities than the Canadian lakes. 4. Our results suggest that severe Canadian winters lead to anoxia under ice and more depauperate fish communities, and stronger zooplankton control on phytoplankton in shallow prairie lakes compared with shallow Danish lakes. If climate change leads to warmer winters and a shorter duration of ice cover, we predict that shallow Canadian prairie lakes will experience increased survivorship of planktivores and stronger control of zooplankton. This, in turn, might decrease zooplankton control on phytoplankton, leading to ‘greener’ lakes on the Canadian prairies.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".