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Record W2186969102 · doi:10.1139/cjfas-2014-0208

Uneven inputs of woody debris to Appalachian streams from superstorm Sandy

2014· article· en· W2186969102 on OpenAlexvenueno aff
Ross G. Andrew, Kyle J. Hartman

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationWest Virginia University
KeywordsSTREAMSDeposition (geology)FluvialDebrisDisturbance (geology)Hydrology (agriculture)Coarse woody debrisEnvironmental scienceAppalachian RegionSnowEcologyPhysical geographyWest virginiaGeologyGeographyHabitatArchaeologySedimentOceanographyGeomorphologyBiology

Abstract

fetched live from OpenAlex

Headwater streams are the beginnings of fluvial networks and therefore fill a critical role in the development of the Earth’s drainages. Therefore, it is important that we understand the role that disturbances have on these systems and how they translate disturbance downstream. Hurricane Sandy struck the eastern seaboard of the United States in late October 2012 and produced record snowfall in the Appalachian Mountains, which caused widespread destruction of trees and subsequent deposition of large wood (LW; pieces ≥1.0 m × 0.05 m) in many headwater streams throughout the region. We investigated these effects in 25 West Virginia headwater streams and found varying levels (0%–195% change from previous annual data; 0–820 LW pieces·km −1 ) of new wood additions. When compared with years prior to Sandy, the rate of LW deposition was significant across all size classes and streams (p < 0.0001). We also found a significantly (p < 0.01) negative pattern of LW impact based upon elevation, with higher elevations receiving lower levels of LW deposition. This research provides a unique glimpse at the initial magnitude of natural wood addition on headwater streams following a large disturbance.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.192
Teacher spread0.183 · 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.

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

Citations56
Published2014
Admission routes1
Has abstractyes

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