Lake Orta chemical status 25 years after liming: problems solved and emerging critical issues
Bibliographic record
Abstract
Lake Orta, located in Piedmont, northwestern Italy, has been severely affected by industrial pollution since the 1930s. A successful liming intervention, performed in 1988-1990, returned pH levels in the lake to neutrality, and accelerated the reduction of aqueous trace metal concentrations. In this paper, we present an update knowledge of the chemical status of Lake Orta, focusing on the data collected from 1990 to 2014. In this period we sampled the lake at its deepest point (Qualba station), on a monthly (1990-2000) or seasonal (since 2001) basis. Samples were collected at nine depths through the water column, and analyzed for pH, conductivity, alkalinity, major ions, nutrients, and trace metals. Collectively, these data allowed us to evaluate the long-term response of the lake to the restoration treatment, with particular regard to its acid-base status; they also provided insights into emerging or potential critical issues, including eutrophication and re-suspension of trace metals that still linger in the lake. Furthermore, the evaluation of the present chemical condition of the lake is a precondition for any successive restoration measure, such as fish introduction. The recent data confirmed the lake’s water quality has recovered, i.e. returned to a pre-pollution chemical state. Lake water values of pH and concentrations of ammonium, sulphate and base cations have stabilized. Alkalinity and nitrate concentrations are also expected to reach stable level in the next few years. Levels of nitrate, reactive silica, and phosphorus compounds are now regulated by algal uptake, providing indirect evidence of a partial biological recovery. For instance, both the inter-annual average decline and the reappearance of a seasonal signal in silica confirmed the presence of a stable diatom community. The lake is presently oligotrophic, and concentrations of both N and P compounds are steady and low throughout the year. However, a monthly check of nutrient levels of the lake and inflowing waters is recommended. The monitoring of base chemical variables, major ions and trace metals should be maintained to assess the overall status of the ecosystem in response to various drivers, including climate change.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".