Relationships between dissolved organic carbon concentrations, weather, and acidification in small Boreal Shield lakes
Bibliographic record
Abstract
We used multiple linear regression analyses to explore empirical relationships between dissolved organic carbon (DOC) concentrations, weather, and acidification in long-term data sets from 12 small Boreal Shield lakes in Ontario, Canada. In two lakes in which pH changes have been very large (4.5 to ~6.0), pH explained most of the temporal variation in DOC concentrations. In the remaining lakes, long-term average previous temperature (on the scale of a decade or more) was usually the best explanatory variable for DOC concentrations. Lake-specific multiple regression models constructed from long-term and short-term attributes of weather (long-term average previous temperature and precipitation, winter–spring precipitation, summer precipitation, summer sunshine) and pH explained between 41% and 96% of the temporal variation in DOC concentrations during the entire monitoring period for these lakes (n = 16–26 years). Multiple regression models considering only the period common to all lakes, 1987 to 2003 (n = 16–17 years), explained 35%–96% of the variation in DOC concentrations. The importance of long-term and short-term attributes of weather in explaining temporal variations in DOC concentrations suggests that changes in climate will have large effects on lake clarity; however, the interactions between weather-related effects may be very complex.
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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 | 0.000 | 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 | 0.001 |
| 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".