Developing Ecosystem Indicators for Responses to Multiple Stressors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human activities in coastal and marine ecosystems provide a suite of benefits for people, but can also produce a number of stressors that can act additively, synergistically, or antagonistically to change ecosystem structure, function, and dynamics in ways that differ from single stressor responses. Scientific tools that can be used to evaluate the effects of multiple stressors are needed to assist decision making. In this paper, we review indicator selection methods and general approaches to assess indicator responses to multiple stressors and compare example ecosystem assessments. Recommendations are presented for choosing and assessing suites of indicators to characterize responses. Indicators should be chosen based upon defined criteria, conceptual models linking indicators to pressures and drivers, and defined strategic goals and ecological or management objectives. Indicators should be complementary and nonredundant, and they should integrate responses to multiple stressors and reflect the status of the ecosystem. An initial core set of indicators could include those that have been tested for the effects of climate and fishing and then expanded to include other pressures and ecosystem-specific, feature-pressure interactions. Identifying indicators and evaluating multiple stressors on marine ecosystems require a variety of approaches, such as empirical analyses, expert opinion, and model-based simulation. The goal is to identify a meaningful set of indicators that can be used to assist with the management of multiple types of human interactions with marine ecosystems.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it