Determining Long-Term Trends of Four Fast-Eutrophicated Lakes in China and Finland
Bibliographic record
Abstract
Lake eutrophication has increased in pace in recent decades and has caused serious environmental problems However, the development trends have not been fully determined as it is difficult to recognize complex effects emanating from both climate and human mechanisms. China has many lakes in different trophic stages, which represent three developing stages from forest- to agriculture-, and then to urban-lake, typically in Lakes Lugu, Taibai, and Taihu. To determine long-term water quality trends, the three lakes were chosen for statistic analysis on dominant effects on the diatom-inferred nutrient changes, and to undertake dynamic modelling regarding climate-controlled nutrient changes. The results indicate the significant turning points of water quality in Lakes Lugu, Taibai and Taihu occurring in the 1990s, 1950s and 1940s respectively, which were effected from human activities by increases in tourism, farming and urbanization respectively. Water quality changes in Lakes Lugu, Taibai and Taihu captured 68.4%, 54.9%, and 86.0% of the temperature variations before the turning points. The anthropogenic impacts explained 84.0%, 96.4% and 96.0% of the water quality variations after the turning points, where the sharp change of water quality by human activity has played an accelerated effect on the gentle change of temperature. Compared with the 4 phases of water quality development in Pyhäjärvi Lake (SW Finland), Lakes Lugu and Taibai have experienced the 1st and 2nd phases, and Taihu has experienced from the 2nd to 3rd phases during the last 150 years. Phase 4 has not occurred in the three lakes, but it is a key period during the eutropication we need to pay attentions.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".