Development of an integrated assessment of large lakes using towed in situ sensor technologies: Linking nearshore conditions with adjacent watersheds
Bibliographic record
Abstract
Coastal and nearshore regions of most large lakes have not been included in monitoring efforts in a regular, consistent and comprehensive fashion. To address this need, we have been developing a survey approach using towed in situ sensors to provide spatially-extensive mapping of nearshore conditions. Within the last decade, we have applied a strategy of towing along the coastline in all five US/Canadian Laurentian Great Lakes. We have developed confidence in the strategy's ability to assess the entire nearshore region comprehensively and efficiently. This article presents an overview of steps of the development, a selection of representative results, and our continuing evaluation of the approach. Findings to date demonstrate an ability to establish linkages between conditions in the nearshore and adjacent watersheds at a variety of spatial scales, including to the US basin-wide level. Results here highlight two plankton sensors (fluorometer for phytoplankton and [laser] optical plankton counter ([L]OPC)) for zooplankton. Results suggested a strong coherence between plankton parameters and a non-linear relationship of plankton metrics to human development of the landscape across the Great Lakes basin.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".