Bibliographic record
Abstract
Abstract. High-resolution numerical ocean models can be used to help interpret sparse observations in the nearshore as well as to help understand the impacts of climate change and extreme events on these dynamically complex coastal areas. However, these high-resolution ocean models require inputs with comparably high resolution, which is particularly difficult to achieve for freshwater discharge. Here, we explored a simple rain-based hydrological model as inputs into a high-resolution (â³13 m) model of Quatsino Sound – a fjord system located on the northwest coast of Vancouver Island, British Columbia, Canada. Through a series of sensitivity tests using an application of the Finite Volume Community Ocean Model (FVCOM version 4.1), we found that model performance was hindered by the lack of knowledge of ungauged rivers and streams. In this case study, including the only major gauged river implied ignoring 538 other watersheds of various sizes and accounted for only about a quarter of the total estimated freshwater discharge. We found that including at least 60 % and ideally closer to 75–80 % of total freshwater fluxes gave similar model performance to including all possible 539 freshwater sources; in our model simulations, this percentage of freshwater flux meant including rivers with watersheds greater than 20–50 km2, or 7–19 total rivers. Further sensitivity tests also indicated that knowing the main outpour locations into the nearshore ocean is an important factor, but not as important as the total freshwater discharge included. Overall, this study illustrates the complexities of studying the land-ocean connection and offers a simple and accessible tool to help address a common problem in nearshore modelling.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.009 |
| Insufficient payload (model declined to judge) | 0.423 | 0.294 |
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".