Determining long-term water quality change in the presence of climate variability: Lake Tahoe (U.S.A.)
Bibliographic record
Abstract
We developed a time series model of Secchi depth for Lake Tahoe incorporating a mechanistic understanding of interannual variability with sufficient simplicity to allow data-based parameter estimation. Secchi depth is still occasionally over 40 m at Lake Tahoe, but mean annual Secchi depth has declined nearly 10 m since 1967, prompting a large-scale restoration program. Year-to-year variability is extremely high, obscuring restoration actions and compliance with water quality standards. The model focused on Secchi depth during summer, when the lake is least transparent and most heavily used. Interannual variability for the summer season is driven largely by precipitation differences. The model offers a means for determining compliance with water quality standards when precipitation anomalies may persist for years. As demonstrated by means of an ex-post forecast, increasing Secchi depths during 1999–2001 were simply climate-driven and do not represent a recovery of the lake. The long-term trend for summer is most likely due to the accumulation of allochthonous mineral suspensoids.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".