Predictive modeling of sediment response to hypoxia in the Gulf of St. Lawrence
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.We use a reaction-transport sediment model to examine the effects of progressive oxygen depletion on sediment geochemistry and fluxes. The model includes physical, geochemical, and biological processes and was calibrated using the geochemical data acquired over the last 30 yr in the lower St. Lawrence River Estuary (Canada). Due to an increased nutrient input, as well as changes in the composition of water masses entering the Gulf of St. Lawrence, the concentration of oxygen in the bottom water at that location has been decreasing at an average rate of 1 mmol/L/yr over the past 70 yr. Modeled benthic fluxes match those obtained in shipboard sediment incubations. For an assumed scenario of further oxygen depletion, we project the fluxes and sediment distributions of iron, manganese, phosphorus, nitrogen, and sulfur for the next 60 years: the fluxes of reduced substances out of the sediment will increase, reactive iron and manganese oxides will become depleted, and the sediment will become progressively enriched in iron sulfides. The projections are sensitive to the effects of oxygen deficiency on benthic organisms. We compare a gradual response of benthic bioturbation/bioirrigation to a threshold-type response and discuss how biological processes in the benthic layer can be parameterized for modeling purposes. As a next step, the sediment model is being coupled to a large-scale hydrodynamic model for the Gulf of St. Lawrence. Accordingly, we discuss the strategies for model adjustments that optimize computer time without sacrificing the accuracy of benthic flux calculations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".