Use of satellite remote sensing tools for the Great Lakes
Bibliographic record
Abstract
Satellite-based sensors provide synoptic measurements of surface parameters useful in detecting physical and biological conditions of the Great Lakes. Satellite surface temperature measurements using infrared spectra are compatible with buoy measurements and the time series now covers more than two decades in length. This time-series tracks seasonal warming patterns and localized upwelling events. The use of visible spectra for remote sensing of water clarity and particle composition is improving with new algorithms to separate chlorophyll-a, inorganic particles, and dissolved organic matter. The use of satellites to measure these variables holds promise for future quantification of phytoplankton production, calcite precipitation (whiting), and suspended sediment from rivers and resuspension events. Satellite imagery has also been useful for interpreting ship-collected data such as those associated with the bi-national Lake Ontario Lower Foodweb Assessment (LOLA) in 2003.
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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 | 0.001 | 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".