When to add a new process to a model – and when not: A marine biogeochemical perspective
Bibliographic record
Abstract
• Deciding whether to include a new process in a model cannot be objective. • An open-access event allowed people to design a flowchart for making this decision. • The flowcharts created highlight many issues that the decision needs to account for. • Variability between flowcharts is used to illustrate subjectivity behind a decision. • There is no perfect flowchart - but creating one's own is itself valuable. Models are critical tools for environmental science. They allow us to examine the limits of what we think we know and to project that knowledge into situations for which we have little or no data. They are by definition simplifications of reality. There are therefore inevitably times when it is necessary to consider adding a new process to a model that was previously omitted. Doing so may have consequences. It can increase model complexity, affect the time a model takes to run, impact the match between the model output and observations, and complicate comparison to previous studies using the model. How a decision is made on whether to add a process is no more objective than how a scientist might design a laboratory experiment. To illustrate this, we report on an event where a broad and diverse group of marine biogeochemists were invited to construct flowcharts to support making the decision of when to include a new process in a model. The flowcharts are used to illustrate both the complexity of factors that modellers must consider prior to making a decision on model development and the diversity of perspectives on how that decision should be reached. The purpose of this paper is not to provide a definitive protocol for making that decision. Instead, we argue that it is important to acknowledge that there is no objectively “best” approach and instead we discuss the flowcharts created as a means of encouraging modellers to think through why and how they are doing something. This may also hopefully guide observational scientists to understand why it may not always be appropriate to include a process they are studying in a model.
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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 | 0.043 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".