Effects of Fisheries Management and Lakeshore Development on Water Quality in Diamond Lake, Oregon
Bibliographic record
Abstract
ABSRTACT Paleolimnological techniques were used to assess water quality changes in a heavily used recreation lake in the Oregon Cascades. Diamond Lake was fishless prior to 1910, but has been intentionally stocked with rainbow trout annually and unintentionally stocked with tui chub in the 1930s and the 1990s. The lake was converted from a mesotrophic system to an eutrophic lake as a consequence of watershed inputs of nutrients associated with shoreline development and biomanipulation in the form of fisheries management. Despite installation of a sewage collection and diversion system, Diamond Lake has increased in sediment accumulation rate and the diatom community has shown an increase in Fragilaria crotonensis and Asterionella formosa, species which are often associated with eutrophication. The two largest increases in sediment accumulation rate and alterations in the diatom community correspond most closely with the two increases in the tui chub population rather than shoreline development. Diatom-inferred (DI) pH increased from 7.95 circa 1910 to over 8.20 in the 1940s. The effects of a rotenone treatment in 1954 to eliminate the tui chub are evident in the short-term decrease in DI- pH and the response of the diatom community. The lake also experienced a major increase in zooplankton abundance in the 20th century as indicated by the remains in the sediment. The results illustrate the need to consider both external and internal sources of nutrients in lake restoration and management attempts.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".