FVCOM model of Shelburne, Nova Scotia
Bibliographic record
Abstract
An FVCOM model was developed for Shelburne, Nova Scotia. The model included tidal and non-tidal forcing at the lateral open boundaries, wind forcing at the sea surface, and fresh water input from the Roseway River. The simulation period was from from 1 August 2014 to 1 January 2015. Model results were compared against observed sea surface height, water currents, temperature, salinity, and density. For the sea surface height, the model underpredicted the M2 tidal amplitude by as much as 6 cm and had high skill scores overall. For the currents, the model was able to capture the vertical structure of the flow. In Jordan Bay the model better predicted the tidal components; in Shelburne Harbour the model better predicted the residual currents. The model consistently overestimated temperature and underestimated salinity with mean biases of -3.12°C and 1.69 ppt. The FVCOM model results were used in a particle tracking model to simulate the deposition of feed pellets and feces on the seabed and the potential spread of bath pesticides used in the treatment of sea lice. Simplified benthic footprints were modelled using two sinking speed and no resuspension, under these conditions the simulated particulate matter remained within the lease boundaries. The simulations of bath pesticide releases predicted three regions of inter-connectivity between farms.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".