Effects of Historical Lake Level and Land Use on Sediment and Phosphorus Accumulation Rates in Lake Kinneret
Bibliographic record
Abstract
Current paradigms of reservoir ontogeny suggest that water-level fluctuations may increase sedimentary nutrient release, causing long-term eutrophication of water bodies formed by dryland flooding. Less is known of the changes in nutrient status following conversion of natural lakes into reservoirs. Here, we use historical hydrological and limnological data and paleolimnological records of sedimentary P accumulation to evaluate changes in nutrient storage in Lake Kinneret, Israel since approximately 1860. Impoundment in 1932 increased water level fluctuations and altered seasonal hydrologic patterns in the lake. Geochemical analysis of sediment deposits indicated that bulk sediment and P accumulation rates in the central lake increased >600% following dam installation (1930s), draining of Lake Hula wetlands (1951-1957), and diversion of surface water outflow (1964 to present). Further, comparison of sedimentary P stratigraphies with long-term chemical records showed that the period of maximum P deposition corresponds to observed increases in whole-lake and in hypolimnetic P content, as well as epilimnetic biological changes indicative of ongoing eutrophication. Together, these patterns suggest that hydrologic management of natural lakes can increase sedimentary nutrient flux under circumstances where lake volume and water levels become more variable.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".