A plankton research gem: the probable closure of the Experimental Lakes Area, Canada
Bibliographic record
Abstract
It will be of widespread concern to the plankton research community that the Canadian government has decided to close the pioneering Experimental Lakes Area (ELA) currently operated by the federal Department of Fisheries and Oceans. The decision has serious implications for the employment of several of Canada's leading freshwater scientists as well as threatening the termination of a number of important on-going experiments. This closure represents a loss not just for the Canadian scientific community but for the international one as well. One of the most critical implications will be the end of data collection for a long-term time series on phytoplankton and zooplankton communities, including important environmental (chemistry, hydrology) variables: an invaluable data set with fortnightly to monthly data from 40 lakes spanning 44 years (15 lakes have time series longer than 20 years). Such data are rare and are the type necessary for synthetic analyses of communities and ecosystems (e.g. Dodson et al., 2000; Jeziorski et al., 2008; Fox et al., 2010; Helmus et al., 2010; Shurin et al., 2010 which all use ELA data) that are increasingly providing insight into the impacts of anthropogenic changes; changes which often require many years to be properly observed and understood.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".