Temporal variations in turbidity in an oil sands pit lake
Bibliographic record
Abstract
, is 9 m deep, and exhibits seasonal thermal stratification similar to that of natural dimictic lakes. Water turbidity was measured continuously throughout the year with moored sensors, supplemented with turbidity profiles and bottle samples, from sites on the lake. During summer there was a gradual exponential (e-folding time of 25 days) decrease in epilimnetic turbidity due to relatively steady settling of solids from the epilimnion through the thermocline to the hypolimnion. Rapid oscillations (periods of approximately 1 day) in turbidity during summer were due to wind driven internal waves. Convective cooling and wind-shear driven stirring during fall storm events increased the turbidity to an annual high just before ice-on. During these storm events, similarity scaling indicated wind shear imparted greater energy than convective cooling to the turbulence at the base of the water column. Ice suppressed wind forcing and resulted in a rapid decrease in turbidity. The rate of decrease in turbidity following ice-on was initially greater than the rate of decrease in the epilimnion during summer, and then slowed until the under-ice turbidity was a relatively constant value which was sustained during the latter half of the ice-on period. The minimum turbidity during winter was greater than the minimum during summer. Following ice-melt in spring, wind driven stirring increased turbidity until summer stratification began.
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 | 0.000 |
| 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) | 0.001 | 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".