Responses of multimetric indices to disturbance are affected by index construction features
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
Multimetric indices (MMIs) are used worldwide to assess the ecological conditions of aquatic and terrestrial ecosystems. Different criteria and approaches are used to construct MMIs, resulting in widely different indices. Therefore, scientists, managers, and policymakers sometimes question whether such MMIs are useful for biomonitoring and bioassessment programs. Crucial design issues for biomonitoring programs include MMI responsiveness, the bioindicator group used, survey design, field sampling methods, level of taxonomic resolution, metric selection and scoring, and reference condition identification. We performed a meta-analysis on MMI development and applications worldwide to analyze the response of MMIs to different disturbance factors and to determine the degree to which MMI construction features influence their responsiveness to anthropogenic disturbances. We used the Web of Science database to find articles that applied an MMI and related MMI values to an environmental stressor, and we extracted data from 157 articles. We performed random-effects modeling to estimate the overall effect of MMI responses to disturbance and used subgroup analysis to analyze the extent to which the effect sizes varied as a function of different MMI construction features. We found that reference condition criteria had the major effect on MMI responses to disturbance. The environmental disturbance type, the number of metrics, and the ecosystem type to which MMIs were applied contributed more weakly to the effect size variance. The general response of MMIs to disturbance was little affected by the bioindicator group, taxonomic resolution, metric selection criteria, or scoring method. These findings have important implications for designing biomonitoring programs, including developing and improving cost-effective biological indices, because they could enhance MMI development and application protocols.
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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) | 0.001 | 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 it