Solving Environmental Problems Using Diatom-Based Estimates of Ph, Nutrients, and Lake Levels
Bibliographic record
Abstract
Serious environmental issues, including acid rain, eutrophication, and decreasing water availability, require knowledge of the: 1) baseline conditions (i.e., what were conditions like before human disturbance); 2) natural variability; and 3) time or level of disturbance when the system responded to the environmental change. This type of knowledge can only be obtained from a historical perspective, which is best achieved through actual measurements of environmental variables. Such records, however, rarely extend more than a few decades, which is usually insufficiently long to determine baseline conditions and natural variability. Diatoms, single celled algae characterized by a cell wall composed of opaline silica, preserved in lake sediments are one of the most widely used paleoindicators, and provide robust estimates of lakewater pH, nutrient concentration and lake level change. A variety of approaches have been developed to infer environmental variables using diatom data, and robust inferences of many environmental variables are now possible. Using paleolimnological techniques, fossil diatoms have been used to track pH, nutrients and lake levels. These records have significantly contributed to our understanding of the causes and impacts of lakewater acidification, eutrophication and hydrologic change, and provide a basis for developing effective management strategies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".