Considering Fish as Recipients of Ecosystem Services Provides a Framework to Formally Link Baseline, Development, and Post-operational Monitoring Programs and Improve Aquatic Impact Assessments for Large Scale Developments
Bibliographic record
Abstract
In most countries, major development projects must satisfy an Environmental Impact Assessment (EIA) process that considers positive and negative aspects to determine if it meets environmental standards and appropriately mitigates or offsets negative impacts on the values being considered. The benefits of before-after-control-impact monitoring designs have been widely known for more than 30 years, but most development assessments fail to effectively link pre- and post-development monitoring in a meaningful way. Fish are a common component of EIA evaluation for both socioeconomic and scientific reasons. The Ecosystem Services (ES) concept was developed to describe the ecosystem attributes that benefit humans, and it offers the opportunity to develop a framework for EIA that is centred around the needs of and benefits from fish. Focusing an environmental monitoring framework on the critical needs of fish could serve to better align risk, development, and monitoring assessment processes. We define the ES that fish provide in the context of two common ES frameworks. To allow for linkages between environmental assessment and the ES concept, we describe critical ecosystem functions from a fish perspective to highlight potential monitoring targets that relate to fish abundance, diversity, health, and habitat. Finally, we suggest how this framing of a monitoring process can be used to better align aquatic monitoring programs across pre-development, development, and post-operational monitoring programs.
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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 | 0.019 | 0.017 |
| 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 | 0.012 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".