Renewal of seasonally anoxic Saanich Inlet is temporally and spatially dynamic
Bibliographic record
Abstract
Injection of oxygenated water into anoxic basins sets off a cascade of biogeochemical reactions and ecosystem shifts. The dynamic nature of these events can create spatial variability in the resulting water mass that strongly affects subsequent observations. Their irregularity can also make their prediction for experiment planning challenging. Our study focuses on Saanich Inlet, one of the most accessible and well-studied anoxic basins in the world. In the late summer and early fall, dense water can cross the sill into this fjord, in discrete events, bringing oxygen and nitrate to the deep waters of the inlet. We assess the potential strength of these renewal events using density measured at a bottom mooring on the sill. We find that the occurrence and potential strength of renewals is primarily controlled by tidal current speeds, which can be well predicted. However, the intensity of coastal upwelling, which brings dense water into the estuarine system, plays a significant secondary control, reducing predictability. We also demonstrate that renewals do not result in a homogeneous water mass filling the deep inlet. Instead, high frequency measurements from a profiling mooring in the centre of the inlet reveal that different densities intruding over the several-day renewal period create a complex layering of waters containing different proportions of new oxic and old anoxic waters. Finally, we show that not every instance of high density water observed over the sill results in flushing of the deepest waters inside the inlet. We hypothesize that each renewal improves the chance of a subsequent renewal in the same season by reducing the density contrast between waters entering and already inside the inlet. Consideration of the temporal and spatial complexity of these renewal dynamics is necessary to support studies using Saanich Inlet as a natural laboratory for exploring oxygen deficient systems.
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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 | 0.000 | 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) | 0.000 | 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".