MétaCan
Menu
Back to cohort
Record W2153691863 · doi:10.1139/f01-166

The impact of beaver impoundments on the water chemistry of two Appalachian streams

2001· article· en· W2153691863 on OpenAlexvenueno aff
Brian E. Margolis, Mark S. Castro, Richard L. Raesly

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
Fundersnot available
KeywordsBeaverTributarySTREAMSHydrology (agriculture)Castor canadensisSink (geography)Acid neutralizing capacityEnvironmental scienceDissolved organic carbonChemistryEnvironmental chemistryEcologyAcid depositionGeologySoil waterSoil scienceGeography

Abstract

fetched live from OpenAlex

We measured the impacts of beaver impoundments on the water chemistry of two headwater streams on the Appalachian Plateau, an unnamed tributary to Herrington Creek (HR), and Mountain Run (MT). We measured acid-neutralizing capacity (ANC), pH, conductivity, discharge, temperature, and the concentrations of major ions, dissolved organic carbon (DOC), and trace metals in stream water upstream and 1 m, 10 m, and 100 m downstream of the beaver impoundments and at two locations, 147 m apart, in a tributary to HR that did not contain a beaver impoundment. There were significant differences in water chemistry upstream and downstream of the beaver impoundments at both MT and HR, but these differences were generally confined to the summer. During the summer, both beaver impoundments generated ANC and increased pH by acting as sinks for NO 3 – and sources of NH 4 + , iron, and manganese. In addition, the beaver impoundment at MT was a sink for SO 4 2– and the impoundment at HR was a source of DOC. The generation of ANC by beaver impoundments may be important to streams of this region where inputs of strong acids from atmospheric deposition are relatively high.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.022
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.216
Teacher spread0.201 · 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 teacher head, 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

Citations71
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicEcology and biodiversity studiesFrench-language works237,207