Emergent technologies and analytical approaches for understanding the effects of multiple stressors in aquatic environments
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
In order to assess how emerging science and new tools can be applied to study multiple stressors on a large (ecosystem) scale and to facilitate greater integration of approaches among different scientific disciplines, a workshop was held on 10–12 September 2014 at the Sydney Institute of Marine Sciences, Sydney, Australia. This workshop aimed to explore the potential offered by new approaches to characterise stressor regimes, to explore stressor-response relationships among biota, to design better early-warning systems and to develop smart tools to support sustainable management of human activities, through more efficient regulation. In this paper we highlight the key issues regarding biological coverage, the complexity of multiply stressed environments, and our inability to predict the biological effects under such scenarios. To address these challenges, we provide an extension of the current Environmental Risk Assessment framework. Underpinning this extension is the harnessing of environmental-genomic data, which has the capacity to provide a broader view of diversity, and to express the ramifications of multiple stressors across multiple levels of biological organisation. We continue to consider how these and other emerging data sources may be combined and analysed using new statistical approaches for disentangling the effects of multiple stressors.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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