Cautions on using the Before-After-Control-Impact design in environmental effects monitoring programs
Bibliographic record
Abstract
Often the Before-After-Control-Impact (BACI) design is suggested as being a statistically powerful experimental design in environmental impact studies. If the timing and location of the impact are known and adequate pre-data are collected, the BACI design is considered optimal to help isolate the effect of the development from natural variability. This paper presents 9 years of results from a long-term BACI experiment tested using a range of statistical models and post-impact monitoring designs. To explore suboptimal designs that are often utilized in environmental effects monitoring, the same data were also explored assuming either no control system was available (Before-After only), or that no pre-impact data were available (Control-Impact only). The results of the BACI design were robust to the statistical model used, and the BACI design was able to detect effects from the impact that the two suboptimal designs failed to detect. However, the BACI design demonstrated different conclusions depending on the number and configuration of post-impact years included in the analysis. Our results reinforce the idea that caution should be employed when using, or interpreting results from, a BACI design in an environmental impact study, but demonstrate that a well-designed BACI remains one of the best models for environmental effects 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 | 0.380 | 0.435 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".