Limnological data derived from high frequency monitoring buoys are asynchronous in a large lake
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
Autonomous data collection is rapidly becoming an integral part of water quality monitoring, particularly for agencies looking to manage and protect aquatic ecosystems. While beneficial, it is unclear how the collection of these data can be applied in spatially complex large lakes (e.g., Laurentian Great Lakes) given the spatial heterogeneity of the ecosystem. To address this potential shortcoming in large lakes, we assessed the synchrony of sensor variables between 10 pairs of static buoys in the western basin of Lake Erie (western basin surface area = 3,282 km2). Within western Lake Erie, water temperature was highly synchronous whereas dissolved oxygen, turbidity, chlorophyll and phycocyanin were asynchronous. The extent of this asynchrony was higher with increasing spatial distance between buoys. We found that between pairs of static buoys, temperature, dissolved oxygen, and turbidity all experienced decreasing correlations with increasing distance. Our results show that if researchers intend to leverage these data to answer important questions and provide real-time applications related to environmental issues like harmful algal/cyanobacterial blooms, monitoring networks need to be designed carefully with spatial complexity in mind. While autonomous data collection has many benefits, the reliance on a single or limited network of anchored monitoring buoys in large lake ecosystems has a high probability of missing important spatial features of these systems.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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