Preface: paleolimnology and lake management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it