3. Quantifying Sediment Deposition Patterns of Lake Underflows Using a Novel Underflow Sediment Trap
Bibliographic record
Abstract
Lake underflow deposition is an important limnological process which greatly affects sediment deposition patterns and lake varve formation. Currently no feasible, cost‐effective device or method has been regularly utilized to quantify sediment deposition patterns. This study utilizes a novel underflow trap which was deployed at two locations at the bottom of a High Arctic lake subject to seasonal river inflow. It was found that a peak in lake bottom temperature departures, lake bottom turbidity, and river suspended sediment concentration are strongly associated with peak underflow deposition events. Furthermore, evidence shows that deposition amounts are greatly reduced as underflow distance increases. One year was also found to show a clear lag in deposition patterns between two distant stations. This method of quantifying underflow deposition is useful for determining deposition patterns over time and space. This knowledge is useful in monitoring the changes in the lake bottom waters, and for aiding in the reconstruction of past sediment deposition patterns.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".