46 Near shore sediment diatoms of the great lakes and their use as biological indicators
Bibliographic record
Abstract
The great Lakes Environmental Indicators (GLEI) project, funded through the U.S. EPA, has set goals to identify taxa that will be useful in determining ecosystem integrity in near shore waters of the Great Lakes ecosystem. Through the development of biological indices, rapid, consistent, and inexpensive methods of bioassessment of the Great Lakes can be utilized. This particular study focuses on the use of near shore sediment diatoms. Sediment core collection and water sampling and analysis were completed at nearly 100 stratified randomly selected sites across all five of the Great Lakes. Canonical correspondence analysis (CCA) indicated a gradient of water quality throughout the Great Lakes. Lake Superior was determined to have the lowest concentration of measured nutrients, while Lake Erie and Lake Ontario had the highest. Lake Michigan and Lake Huron shared a similar transitional water quality. Although analyses and indices have been developed thus far, GLEI is in its third year of a 4‐year schedule.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".