How do we take the pulse of an aquatic ecosystem? Current and historical approaches to measuring ecosystem integrity
Bibliographic record
Abstract
Global environmental monitoring has indicated that the structure and function of some aquatic ecosystems has been significantly altered by human activities. There are many potential causes for these changes; however, one major concern is the increasing release of anthropogenic contaminants into aquatic environments. Although toxicological responses of individual organisms are typically well characterized, few studies have focused on characterizing toxicity at the ecosystem level. In fact, because of their scale and complexity, changes in ecosystem integrity are rarely considered in assessments of risks to ecosystems. This work attempts to move the conversation forward by defining integrity of ecosystems, reviewing current and historical approaches to measuring ecosystem integrity status (e.g., structural and functional measurements), and highlighting methods that could significantly contribute to the field of ecosystem toxicology (e.g., keystone species, environmental energetics, ecotoxicological modeling, and adverse outcome pathways [AOPs]). Through a critical analysis of current and historical methodologies, the present study offers a comprehensive, conceptual framework for the assessment of risks of contaminant exposure for whole ecosystems and proposes steps to facilitate better diagnoses of the integrity of aquatic systems. Environ Toxicol Chem 2019;38:289-301. © 2018 SETAC.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".