A long-term water quality and meteorological data set (2014 – 2021) of a eutrophic prairie lake: Buffalo Pound Lake, Saskatchewan, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.011 | 0.023 |
| Research integrity | 0.001 | 0.002 |
| 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 it