Analyzing long‐term water quality of lakes in Rhode Island and the northeastern United States with an anomaly approach
Bibliographic record
Abstract
Abstract Addressing anthropogenic impacts on aquatic ecosystems is a focus of lake management. Controlling phosphorus and nitrogen can mitigate these impacts, but determining management effectiveness requires long‐term datasets. Recent analysis of the LAke multi‐scaled GeOSpatial and temporal database for the Northeast (LAGOS‐NE) United States found stable water quality in the northeastern and midwestern United States; however, sub‐regional trends may be obscured. We used the University of Rhode Island’s Watershed Watch Volunteer Monitoring Program (URIWW) dataset to determine if there were sub‐regional (i.e., 3000 km 2 ) water quality trends. URIWW has collected water quality data on Rhode Island lakes and reservoirs for over 25 yr. The LAGOS‐NE and URIWW datasets allowed for comparison of water quality trends at regional and sub‐regional scales, respectively. We assessed regional (LAGOS‐NE) and sub‐regional (URIWW) trends with yearly median anomalies calculated on a per‐station basis. Sub‐regionally, temperature and chlorophyll a increased from 1993 to 2016. Total nitrogen, total phosphorus, and the nitrogen:phosphorus ratio (N:P) were stable. At the regional scale, the LAGOS‐NE dataset showed similar trends to prior studies of the LAGOS‐NE with chlorophyll a , total nitrogen, and N:P all stable over time. Total phosphorus did show a very slight increase. In short, algal biomass, as measured by chlorophyll a in Rhode Island lakes and reservoirs increased, despite stability in total nitrogen, total phosphorus, and the nitrogen to phosphorus ratio. Additionally, we demonstrated both the value of long‐term monitoring programs, like URIWW, for identifying trends in environmental condition, and the utility of site‐specific anomalies for analyzing for long‐term water quality trends.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".