A long-term water quality and meteorological data set (2014 – 2021) of a eutrophic prairie lake: Buffalo Pound Lake, Saskatchewan, Canada
Bibliographic record
Abstract
Lakes can undergo rapid changes that are not captured during traditional, discrete sampling campaigns. Sensor-based data provided opportunities to understand these rapid changes in lakes. Here, we present 8 years of sensor-based monitoring data from the open water season in a shallow, polymictic reservoir in southern Saskatchewan, which serves as an important drinking water supply. A monitoring buoy was moored at a single location, providing sensor data, including water temperature, photosynthetically available radiation (PAR), pH, dissolved oxygen, specific conductivity, turbidity, phycocyanin and chlorophyll at 2 depths (0.8 and 2.8 m below surface), and temperature throughout the water column, at 10-minute intervals timeframe. The buoy also had a weather station, recording air temperature, barometric pressure, PAR, rain, relative humidity, wind direction and wind speed. Data were reviewed and graded for data quality. This long-term dataset can be used to understand thermal variation and varied, often rapid, changes that polymictic lakes undergo, particularly through seasonal changes and development of cyanobacterial blooms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".