DOI 10.1007/s11269-011-9792-3 Monitoring Lake Simcoe Water Clarity Using Landsat-5 TM Images
Bibliographic record
Abstract
Abstract This study focuses on utilizing satellite remote sensing to monitor the water clarity of Lake Simcoe, Ontario, Canada, which has been suffering from the overload of phosphorus (TP) and therefore eutrophication for decades. The dataset includes 22 cloud-free Landsat-5 Thematic Mapper (TM) images, as well as the nearly simultaneous in-situ observations from 15 stations on the lake. Compared to the general model used to estimate the Secchi Disk Transparency (SDT), a parameter for water clarity measurements, an improved model is developed, from the TM images. The results of these estimations are validated using the in-situ data by linear regression, and the accuracies are measured by the coefficient of determination R2. The results reveal an indication of high model fit between the majority of SDT predictions and the in-situ observations. Also, the improved SDT model provides higher prediction accuracies than the general one when applied to 68.2 % (15 out of 22) of the images. The estimated clarity maps indicate that the turbid water is normally distributed at the nearshore areas and the northeastern region. Meanwhile, the southwestern lake has much clearer water than the other regions. In addition, the
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.645 | 0.650 |
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".