Marine ecosystems model development should be rooted in past experiences, not anchored in old habits
Bibliographic record
Abstract
Abstract Numerical models of marine ecosystems tend to increase in complexity, incorporating a growing number of functions and parameters. Here, we reflect on the issue of “anchoring” inherent to model development, i.e. the tendency for modellers to take processes, functional forms and parameters from previous studies as granted. We focused on the particular example of the parameterization of temperature-dependent ontogeny in Calanus spp. copepods. We could identify 68 studies that implemented and parameterized this functional relationship. Semantic analysis identified distinct clusters of research scopes and coauthor networks. We showed that biases in parameters origin have the potential to produce misleading results, while recent experimental studies were often not assimilated into contemporary modelling studies. Anchoring involves external constraints in numerical models' development such as conceptual gaps and data scarcity, as well as internal drivers such as academic ontogeny and cultural background of the modeller. Retrospective quantitative literature analyses help identify how biases have worked their way into the collective understanding and help to suggest ways forward for the research community. These involve implementation of revision management systems for parameters and functional forms as already exists for numerical codes, and, as always, a more efficient dialogue between modellers, experimentalists and field ecologists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".