A multiscale approach to water quality variables in a river ecosystem
Bibliographic record
Abstract
Abstract Monitoring select ecosystem variables across time and interpreting the collected data are essential components of ecosystem assessment supporting management. Increasingly affordable sensors and computational capacity have made very large dataset assembly more common. However, these datasets initiate analytical challenges by their size and theoretical challenges due to the scale of the processes they encompass. Multiscale assessment of high temporal resolution water quality sensor data (temperature, in vivo chlorophyll a, colored dissolved organic matter) collected year‐round was conducted for the Upper St. Lawrence River. Using numerical methods that directly integrate the concept of scale, we show that consideration of scale‐dependent processes can lead to increased predictive power and a clearer understanding of ecosystem function. These results suggest that multiscale methods are not only an alternative way of approaching long‐term data assessment, but also a necessity in order to avoid spurious interpretation. Consequently, the concept of scale as described here can be consistently integrated into long‐term data studies to assist in the interpretation of high‐resolution data that help describe natural phenomena in aquatic systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".