Linkages between weather, dissolved organic carbon, and cold-water habitat in a Boreal Shield lake recovering from acidification
Bibliographic record
Abstract
To investigate potential effects of climate change on lake thermal structure, we examined relationships between the amount of cold-water habitat in late summer (defined as the 10 °C depth), summer weather, and dissolved organic carbon (DOC) concentration over a two-decade period (19812002) in a small Boreal Shield lake recovering from acidification. DOC concentration, wind-days (the product of mean daily wind speed and the number of days between ice-out and late-summer stratification), and mean daily temperature were significant predictors of the 10 °C depth in a multiple-regression model. A similar model using simply the number of ice-free days instead of wind-days was almost as effective. The models were quite successful in explaining interannual variations in the 10 °C depth when tested on a chemically and morphometrically similar nearby lake. While factors related to summer weather were important in explaining interannual variations in the amount of late-summer cold-water habitat, increased DOC concentration over the study period largely explained observed long-term decreases in the 10 °C depth (increases in cold-water habitat). DOC concentration was positively correlated with pH. In acidified regions, increases in DOC that accompany the recovery of acidified lakes will need to be considered in assessments of potential climate-change effects on lake thermal structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".