Field Measurements of Bulk Flow and Transport through a Small Coastal Embayment Having Variable Distributions of Aquatic Vegetation
Bibliographic record
Abstract
This thesis is largely the documentation of two passive tracer release studies performed in Sterling Pond (SP), a small embayment on the southern coast of Lake Ontario (LO). SP has a large watershed and is strongly connected to LO by a long and narrow channel. The experiments were designed to decipher the effects of aquatic vegetation (macrophytes) on flow and transport through SP and through shallow embayments dominated by macrophytes in general. Towards this objective, the studies captured the residence time distribution (RTD) of water entering SP from its watershed under two different spatial distributions of macrophytes, and they were conducted in synchrony with extensive surveys of macrophyte density, height, and species composition. Variables relevant to the dynamics of SP were continuously monitored at its boundaries, including meteorological conditions, water surface elevation, flow, and temperature (temperature was continuously monitored within SP as well); bathymetric data was collected once. The first study took place when watershed flow was high and macrophytes were sparse -- mean residence time was measured to be 0.6days. The second took place under moderate barotropic forcing from LO when watershed flow was low and macrophytes were dense and uniform across SP -- mean residence time was measured to be 20days. As the studies were conducted under fairly extreme environmental conditions, the measured RTD's roughly capture the range of residence time scales of water entering SP from its watershed. The tracer experiments, macrophyte surveys, and auxiliary data comprise a benchmark data set that may be used for development and validation of numerical models of flow through flexible vegetation.
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".