Modeling the influence of North Atlantic freshening on phytoplankton dynamics in the Nova Scotian Shelf and Gulf of Maine region
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Continental shelf waters from the Labrador Sea to the Mid-Atlantic Bight experienced significant freshening in the late 1990s, likely caused by increasing glacial melting and enhanced precipitation and river runoff at higher latitudes (as a result of climate change). The freshening of the ocean can alter circulation and stratification of shelf waters and may influence the phytoplankton dynamics and ecosystem productivity. We used a 3-D coupled biological-physical model to examine the influence of freshening on the timing and magnitude of phytoplankton blooms and primary productivity in the Gulf of Maine/Scotian Shelf region. The model captured the general pattern of westward propagation of spring phytoplankton blooms from the Scotian Shelf to the western Gulf of Maine, consistent with the observed increasing sea surface salinity and associated decreasing stability of the water column. By adjusting the boundary conditions in the numerical experiments, the model showed that increased freshening can further enhance the spatial gradients in timing by stimulating earlier blooms upstream (the Scotian Shelf), but has less impact downstream (the western Gulf of Maine). The model results suggest that surface water freshening may impede winter convection and decrease nutrient supply from deep water to the surface, thus influencing the overall seasonal primary productivity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".