A simple one-dimensional NPZD model with graphical user interface
Bibliographic record
Abstract
This github code is a simple, 1-dimensional NPZD model in Matlab, based on Kuhn et al. (2015, doi.org/10.1016/j.pocean.2015.07.004), with a Graphical User Interface (GUI). The model is representative of a location at 50 degree N in the North Atlantic Ocean and the mixed layer evolution from this location is imposed. The model is run for 2 years, but only the 2nd year is shown in the auto-generated plots. The satellite-observed surface phytoplankton evolution at this location is shown for comparison in the surface property plot. The GUI is called from the Matlab command line as follows: >> GUI_NPZD A window will pop up that allows the user to modify 4 parameters: the latitude of solar forcing, the initial nutrient concentration, the maximum phytoplankton growth rate, and the maximum zooplankton grazing rate. These parameters can be adjusted by using the sliders or typing new values directly into the corresponding editbox. If the value entered is outside the allowable range indicated by the slider, it will be reset to the corresponding allowable maximum or minimum. The buttons below the four sliders allow the user to run the model or quit the GUI. The evolution of state variables at the surface layer will be written into a Matlab file along with essential meta-information. The code is also used in the article "Ocean Biogeochemical Modelling" in Nature Review Methods Primers by Fennel et al. (2022).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".