WHAT INFLUENCES VALUED ECOSYSTEM COMPONENT SELECTION FOR CUMULATIVE EFFECTS IN IMPACT ASSESSMENT?
Bibliographic record
Abstract
Despite the central role of valued ecosystem component (VEC) selection to project impact assessment (IA) and cumulative effects assessment (CEA), little is known about what influences it. To potentially improve the efficacy of CEA, a look into the "black box" of VEC selection is warranted. An investigation of eleven road construction project IAs in Canada completed between 1995 and 2011 via document analysis and interviews with project informants reveals a heavy reliance on residual effects analysis for CEA VEC selection, such that project VEC list are often exactly or nearly the same as CEA VEC lists. The process of VEC selection is highly subjective, lacking in scientific inputs, and not as sensitive to cumulative effects issues as it perhaps should be given the nature of road projects. The study concludes that a "residual effects analysis—plus" approach to CEA VEC selection is desirable, along with explicit, possibly sector-specific, guidance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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".