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Record W2109943687 · doi:10.5539/ep.v1n2p124

Failure of Reach-Scale Restoration to Improve Biotic Integrity in a Mid-Atlantic Stream

2012· article· en· W2109943687 on OpenAlexvenueno aff
William J. Meisenbach, Helle Tychsen, Christina Y. S. Siu

Bibliographic record

VenueEnvironment and Pollution · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
FundersPennsylvania Department of Environmental Protection
KeywordsRestoration ecologyBenthic zoneEnvironmental scienceWatershedScale (ratio)Stream restorationHabitatSTREAMSEcologyEnvironmental resource managementGeographyBiologyComputer scienceCartography

Abstract

fetched live from OpenAlex

Reach-scale restoration to re-establish habitat integrity is practiced throughout the US. These techniques, while yielding aesthetic enhancement, may not result in ecological improvement. Using a before-after, control-impacted (BACI) sampling design, we evaluated the benthic macroinvertebrate community in two branches of the Codorus Creek, Southeastern PA throughout the course of restoration projects. Reach-scale restoration activities at several sites along both branches have been conducted since 2002. There was no significant change in the benthic macroinvertebrate community associated with reach-scale restoration. Even after the restoration projects were completed, downstream-impacted sites on both branches reflected severe stress with reduced numbers of organisms, low diversity, and impoverished pollution sensitive species. Thus, our results indicate that there were no significant short-term benefits resulting from reach-scale restoration. We recommend that the actions of individuals and organizations concerned with streams focus on watershed-scale conservation and restoration activities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.208
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

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