Preface: paleolimnology and lake management
Bibliographic record
Abstract
Paterson AM, Köster D, Reavie ED, Whitmore TJ. 2020. Preface: paleolimnology and lake management. Lake Reserv Manage. 36:205–209. Paleolimnology uses information preserved in lake, river, and wetland sediments to understand past environmental conditions. Paleolimnologists access and analyze records of environmental change that have been temporally and spatially integrated over decades to centuries. These data provide a powerful complement to monitoring programs or shorter term studies that are unable to evaluate predisturbance conditions. The present-day environment is a product of the natural geologic setting and past human influences, and environmental stressors affect lakes over long time periods. Consequently, lake managers have recognized the value of paleolimnology for assessing long-term impacts from environmental stressors, and for establishing management baselines or reference conditions. This special issue on paleolimnology and lake management explores 7 examples from lakes across North America that show the value of paleolimnology in providing a long-term perspective on environmental change.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.094 | 0.047 |
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".