OLIGOTREND, a global database of multi-decadal chlorophyll <i>a</i> and water quality time series for rivers, lakes, and estuaries
Bibliographic record
Abstract
Abstract. Reversed eutrophication, called oligotrophication, has been widely documented globally over the last 30 years in rivers, lakes, and estuaries. However, the absence of a comprehensive and harmonized dataset has hindered a deeper understanding of its ecological consequences. To address this data gap, we developed the OLIGOTREND database, which contains multi-decadal time series of chlorophyll a, nutrients (nitrogen and phosphorus), and related physicochemical parameters, totalling 4.3 million observations. These data originate from 1894 unique monitoring locations across estuaries (n = 238), lakes (687), and rivers (969). Most time series cover the period from 1986–2022 and comprise at least 15 years of chlorophyll a observations. Each location is associated with catchment and hydroclimatic attributes. Trend and breakpoint analyses were applied to all time series. Chlorophyll a showed temporally variable and ecosystem-specific responses to nutrient declines with an overall declining trend for 18 % of the time series, contrasting greatly with a majority of declining trends for nutrient concentrations. We harmonized the database to ensure reproducibility and ease of access and support future updates and contributions. Available at https://doi.org/10.6073/pasta/a7ad060a4dbc4e7dfcb763a794506524 (Minaudo and Benito, 2024), the OLIGOTREND database supports collaborative efforts aimed at further advancing our understanding of biogeochemical and biological mechanisms underlining oligotrophication and ecological impacts of global long-term environmental change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".