Combining palaeolimnological and limnological approaches in assessing lake ecosystem response to nutrient reduction
Bibliographic record
Abstract
Summary 1. Palaeolimnological data and limnological time‐series data are highly complementary. Sediment records extend time‐scales, integrate subannual variability and expand the range of sites that can be studied, but they suffer from taphonomic biases and occasionally from uncertain chronology. Observational time‐series data, on the other hand, are highly resolved but are very limited in extent both in space and time. 2. Palaeolimnological and observational data‐sets need to be combined in oligotrophication research to establish (i) the past and present status of lakes needed to identify reference conditions; (ii) changes in ecosystem state; (iii) responses to nutrient reduction; and (iv) the potential role of other factors (e.g. additional stressors, climate change) that might confound predictions of future state.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".