Diatom-inferred dissolved organic carbon reconstructions provide assessments of past UV-B penetration in Canadian Shield lakes
Bibliographic record
Abstract
Paleolimnological approaches have provided strong inference models for lake-water acidity and metal concentrations in Sudbury-area lakes, but calibration data have not yet been explored for inferring lake-water dissolved organic carbon (DOC). A review of available limnological data provided DOC values for 80 of our 105 calibration lakes, allowing us to examine the relative importance of DOC in determining the distribution of diatom assemblages in Canadian Shield lakes, such as those in the Sudbury region. Canonical correspondence analysis (CCA) indicated that lake-water DOC explained a significant proportion of variation in the diatom data. Our weighted-averaging DOC model showed a strong relationship (r2 = 0.63) between measured and inferred DOC. Using this model, we reconstructed historical DOC concentrations in three Sudbury-area lakes, and then estimated temporal changes in UV-B penetration. These reconstructions showed that lake water DOC concentrations and underwater UV-B penetration have changed markedly in two of the three lakes modeled. The close correspondence between inferred and measured DOC for the 1980s in all three lakes provides further evidence that we have accurately inferred DOC concentrations. Quantitative DOC reconstructions in Sudbury-area lakes offer an excellent technique for assessing past changes in DOC concentrations and UV-B penetration in Canadian Shield lakes.
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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 | 0.001 | 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) | 0.001 | 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".