A physicalâbiogeochemical coupling scheme for modeling marine coastal ecosystems
Bibliographic record
Abstract
Ecological modeling of dynamic systems such as marine environments may require detailed spatial resolution when the modeled area is greatly influenced by complex physical circulation. Therefore, the simulation of a marine ecosystem must be underlain by a physical model. However, coupling hydrodynamic and biogeochemical models is not straightforward. This paper presents a modeling technique that can be used to build generic and flexible fully-spatial physical–biogeochemical models to study coastal marine ecosystems using a visual modeling environment (VME). The model core is constructed in Simile, a VME that has the capacity to create multiple instances of submodels that can be interconnected, producing a fully-spatial simulation. The core is designed to assimilate a choice of different hydrodynamic models by means of matrices, enhancing its compatibility with different software. The biogeochemical model can be modified by means of a graphical interface, which facilitates sharing within the scientific community. This paper demonstrates the application of the coupling scheme to mussel aquaculture in Tracadie Bay (PEI, Eastern Canada). The model was run for two different years, 1998 and 1999, and indicated that mussel biomass exerts a top-down control of phytoplankton populations, causing a maximum chlorophyll depletion of 61.0% and 80.3% for 1998 and 1999 respectively. The difference between both years highlights the importance of inter-annual variability, which is significant from an ecosystem-level perspective because it reveals the relevance of applying a precautionary policy in the management of aquaculture activity. Therefore, the proposed core developed in Simile is a generic and flexible tool for modeling long-term processes in coastal waters, which is able to assimilate a choice of hydrodynamic models, constituting a novel approach for generating fully-spatial models using visual modeling environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".