Bibliographic record
Abstract
Abstract In dimictic lakes, the stable density stratification during summer and winter inhibits vertical mixing of nutrients and oxygen. This favors the development of hypolimnetic hypoxia, which degrades cool‐water fish habitat and enhances nutrient mineralization from the sediments. Fall and spring turnover events, therefore, provide a crucial biannual link between surface and bottom waters. However, the physical processes occurring to mix lakes during turnover events remain comparatively uninvestigated. In this study, long‐term field observations were supplemented with output from a three‐dimensional numerical model, to better understand turnover events within a small temperate lake during 2011–2017. Mid‐basin penetrative convection, sidearm convection, and wind‐induced mixing were identified, with mid‐basin convection likely contributing the most to turnover events; a typical side‐arm convective plume had 2% (spring) to 4% (fall) of the mass flux compared to those at mid‐basin. During fall turnover, wind shear only mixed the upper 35% of the surface mixed layer, with convection acting to deepen below. During spring turnover, ice‐cover sheltered the lake from wind, causing convection to be the only process occurring and lengthening the turnover duration (~ 51 d) compared to fall (~ 13 d).
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".