A new marine ecosystem model for the University of Victoria Earth System Climate Model
Bibliographic record
Abstract
Abstract. Earth System Climate Models (ESCMs) are valuable tools that can be used to gain a better understanding of the climate system, global biogeochemical cycles and how anthropogenically-driven changes may affect them. Here we describe improvements made to the marine biogeochemical ecosystem component of the University of Victoria's ESCM (version 2.9). Major changes include corrections to the code and equations describing phytoplankton light limitation and zooplankton grazing, the implementation of a more realistic zooplankton growth and grazing model, and the implementation of an iron limitation scheme to constrain phytoplankton growth. The new model is evaluated after a 10 000-yr spin-up and compared to both the previous version and observations. For the majority of biogeochemical tracers and ecosystem processes the new model shows significant improvements when compared to the previous version and evaluated against observations. Many of the improvements are due to better simulation of seasonal changes in higher latitude ecosystems and the effect that this has on ocean biogeochemistry. This improved model is intended to provide a basic new ESCM model component, which can be used as is or expanded upon (i.e., the addition of new tracers), for climate change and biogeochemical cycling research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".