A Multi-Year Assessment of Phytoplankton Fluorescence in a Large Temperate River Reveals the Importance of Scale-Dependent Temporal Patterns Associated With Temperature and Other Physicochemical Variables
Bibliographic record
Abstract
An integrated temporal study of a long-term ecological research and monitoring database of the St. Lawrence River was carried out. A long and mostly uninterrupted high temporal resolution record of fluorometric data from 2014 to 2018 was used to examine phytoplankton fluorometric variables at several scales and to identify temporal patterns and their main environmental drivers. Sets of temporal eigenvectors were used as modulating variables in a multiscale codependence analysis to relate the fluorometric variables and various environmental variables at different temporal scales. Fluorometric patterns of phytoplankton biomass in the St. Lawrence River are characterized by large, yearly-scale patterns driven by seasonal changes in water temperature, and to a lesser extent water discharge, over which finer-scale temporal patterns related to colored dissolved organic matter and weather variables can be discerned at shorter time scales. The results suggest that such an approach to characterize phytoplankton biomass in large rivers may be useful for processing large data sets from remote sensing efforts for detecting subtle large-scale changes in water quality due to land use practices and climate change.
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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".