Does restoration work? It depends on how we measure success
Bibliographic record
Abstract
The restoration of 4 partial stream barriers was evaluated in watersheds of Terra Nova National Park, Newfoundland, Canada from 2009 to 2011. Brook trout (n = 462) were tagged and tracked moving through our study sites using passive‐integrated transponder telemetry and the restoration actions were assessed using 3 different measures: passage success rates; the range of passable flows; and the availability of passable flows. We considered the observed results within a before‐after‐control‐impact (BACI) design that included reference reaches and pre‐restoration observations. The conclusions of BACI analyses were also contrasted with those that would have been obtained from commonly used before‐after (B‐A) or control‐impact (C‐I) study designs. While the restoration actions changed hydrological conditions in a way that should facilitate fish passage, our biological measures indicated that success was variable across culverts and within culverts depending on the measure evaluated. Furthermore, the natural temporal and spatial variability of fish movements often resulted in different conclusions between the more robust BACI design and the more commonly used B‐A and C‐I designs. Our results demonstrate that restoration of partial barriers may not always yield dramatic improvements. Furthermore, without suitable controls, the chances of drawing false conclusions regarding restorations in temporally and spatially dynamic systems are substantial.
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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 | 0.039 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".