Predicting impacts of agricultural land use on stream and river biota: method review, evaluation, and guidance
Bibliographic record
Abstract
Predicting land-use and land-management effects on stream and river biota is an important aspect of land-water management, yet there are no collations of what methods are available to carry out those assessments nor guidance on which methods to use. This paper summarises a range of methods with examples of their applications, comments on their strengths and weaknesses, evaluates them against a set of criteria, and provides guidance on method selection. Assessment methods include empirical statistical and mechanistic models, Bayesian networks, likelihood–consequence risk assessments, scoring methods, and hybrid methods, some of which can be informed by expert elicitation. An evaluation matrix for methods indicated that no single method is ideal, and selection of methods needs to carefully consider factors such as the physico-chemical stressor or biotic impact of interest, the intended stakeholders, and the scales of assessment. One emergent principle is the separation of relationships between land use and stressors from assessments of stressors and biota, for which alternative methods could be used. A tiered approach is recommended, whereby simple methods with low resource and time requirements are applied first, followed by more sophisticated methods for selected aspects if needed. There is a need for more ready-made methods at the screening level, as well as development of new methods to address remaining gaps such as multiple stressors.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".