Individual-Based Model use in Marine Policy
Bibliographic record
Abstract
Individual-based models (IBMs) are increasingly used in marine conservation research, making this is an ideal time to assess IBM use in marine policy. IBMs can contribute important information to marine management and policy questions, as they offer complex methods of understanding ecosystems and animal behaviour, by allowing for heterogeneity in both individuals and environments. A review of 108 international peer-review publications utilizing marine IBMs was conducted using Web of Science (WoS). It was determined that 55% of the WoS articles claimed that the IBMs were relevant or important to marine conservation policy or management. A relevant English-language policy document was located for 83% of the IBMs, but only 32% were cited, while 85% of the same policy documents cited a different, non-IBM, modelling method. A separate survey of 175 policy documents from the Government of Canada was conducted. Of the 60 that contained citations, zero documents cited an IBM, while 75% cited a different modelling method. Of 407 webpages reviewed from the National Oceanic and Atmospheric Administration, the New Zealand Department of Conservation, and the UK Government website, only 4% referenced IBMs. This research demonstrates that, despite claims of usefulness by researchers, IBMs are not used to inform policy, while other model methods are commonly cited. Modellers should not assume that their model will inherently be useful for policy and should instead ensure that they are: 1) addressing a policy need; and 2) making the information accessible to policymakers by crafting a communication plan and/or joining a relevant boundary organization.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.126 | 0.087 |
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; both teacher heads agree on what is shown here.
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".