Conceptual Models for Marine and Freshwater Systems in Alaska: Flexible Tools for Research Planning, Prioritization and Communication + Supplementary Appendices (See Article Tools)
Bibliographic record
Abstract
Conceptual ecological models synthesize information about complex systems into simplified visual maps and can be used to prioritize system components for research or management attention. In this article, we introduce conceptual modeling methods that incorporate expert ratings about a suite of properties of system components, including assessment of the state of knowledge, the strength of ecological impact, and the state of management or research attention devoted to a given component. Quantitative ratings of the properties of system components are subsequently used to prioritize model components objectively for research or management attention. Two case studies, one on plankton-herring-baleen whale dynamics and one on Chinook salmon strategic research planning, are presented to illustrate techniques. For example, in the Chinook salmon case study, participants constructed a prioritization score that identified system components rated as high ecological impact, but low state of knowledge and low state of management or research attention. By addressing gaps in both knowledge and attention, participants implemented a strategy for research planning that complemented existing Chinook salmon research and management in the study region. The case studies demonstrated that conceptual ecological models could be completed successfully with an economy of time. Conceptual modeling has been implemented across a range of disciplines and provides a useful tool that natural resource management and research groups can use to organize collaborative efforts and communicate research or management progress to stakeholders or funders.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 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".