New approaches to the ecological risk assessment of multiple stressors
Bibliographic record
Abstract
So as to assess how emerging science and new tools can be applied to study multiple stressors at a large (ecosystem) scale and to facilitate greater integration of approaches among different scientific disciplines, a workshop was organised on 10–12 September 2014 at the Sydney Institute of Marine Sciences, Sydney, Australia. The present paper discusses the limitations of the current risk-assessment approaches and how multiple stressors at large scales can be better evaluated in ecological risk assessments to inform the development of more efficient and preventive management policies based on adaptive management in the future. A future risk-assessment paradigm that overcomes these limitations is presented. This paradigm includes cultural and ecological protection goals, the development of ecological scenarios, the establishment of the relevant interactions among species, potential sources of stressors, their interactions and the development of cause–effect models. It is envisaged that this will be achievable through a greater integration of approaches among different scientific disciplines and through the application of new and emerging tools such as 'big data', ecological modelling and the incorporation of ecosystem service endpoints.
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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".