Bibliographic record
Abstract
Aquatic ecosystems are constantly changing due to natural and anthropogenic stressors. When dealing with such 'moving targets', one of the greatest challenges faced by scientists, managers and policy makers is to use appropriate time scales for environmental assessments. However, most aquatic systems lack monitoring data, and if a programme does exist, rarely have data been collected for more than a few years. Hence, it is often difficult or impossible to determine the nature and timing of ecosystem changes based on these short-term datasets. Furthermore, as environmental assessments are typically performed after a problem is identified, critical data regarding pre-disturbance (or reference) conditions are rarely available. Here, I summarize some recent studies employing lake sediment analyses (i.e. palaeolimnology) that have provided retrospective assessments of ecosystem changes that have been emerging slowly and often innocuously 'under the radar'. My examples include the identification of legacy effects of acid rain and logging, namely long-term declines in calcium concentrations in softwater lakes, which have led to significant repercussions for ecosystem services. I then show that past trajectories of aerial pollution from the burgeoning oil sands operations of western Canada can be tracked using environmental proxies preserved in dated sediment cores, and how these data can be used to determine the relative contributions of natural versus industrial sources of pollutants. I conclude by reviewing how palaeolimnological analyses have linked climate change with the proliferation of harmful blue-green algal (cyanobacterial) blooms, even without the addition of limiting nutrients. Collectively, these studies show that effective ecosystem management, particularly for incremental environmental stressors, requires temporal sampling windows that are not readily available with standard monitoring, but can be supplemented with high-resolution lake sediment analyses.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".