Projecting Future Climate States for the Salish Sea in Support of the Management of local Ecosystems and Fisheries
Bibliographic record
Abstract
Projections of the future ocean state for Canada’s Salish Sea are necessary to understand the effects of climate change on ecosystems, fisheries, and aquaculture providing critical information on likely future conditions and the ability to adaptively manage fisheries resources, ecosystems and significant areas. A multi-stage downscaling system is under development to improve our understanding of climate impacts for the Salish Sea and to generate actionable climate data for decision makers. Climate models are global and, therefore, are restricted to coarse resolutions on the order of 100km. The narrow straits and channels and complex bathymetry of the Salish Sea are modelled using a high-resolution ocean model known as the SalishSeaCast (500m). To drive the ocean model, the atmospheric climate forcing will be downscaled to a meaningful resolution. This project compares statistical and dynamical downscaling methods for downscaling the driving atmospheric fields to determine which method produces a more realistic ocean state. The downscaled fields will be used to analyze changes between a hindcast period (1986-2005) and future period (2046-2065) under two climate scenarios: the moderate mitigation representative concentration pathway (RCP) 4.5 and the no mitigation scenario RCP 8.5. The SalishSeaCast will be enhanced with a module for benthic respiration to improve the representation of biogeochemistry. The study will quantify changes in key stressors (e.g. temperature, oxygen, pH) and create maps of change to highlight ecologically significant areas for the purposes of conservation and protection. Additionally, we will investigate the impact of changing freshwater discharge on the Salish Sea marine ecosystem by conducting sensitivity tests using the recently available hydrological model projections of future discharge from the Fraser River under both future scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".