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Record W2003075149 · doi:10.1139/f02-129

Ice storm impacts on woody debris and debris dam formation in northeastern U.S. streams

2002· article· en· W2003075149 on OpenAlexvenueaboutno aff
Clifford E. Kraft, Rebecca L. Schneider, Dana R. Warren

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
FundersU.S. Forest Service
KeywordsSTREAMSCoarse woody debrisDebrisRiparian zoneLarge woody debrisHydrology (agriculture)StormEnvironmental scienceCanopySnagDeposition (geology)GeologyHabitatEcologyGeomorphologyOceanographySediment

Abstract

fetched live from OpenAlex

In January 1998, an ice storm damaged forests in northeastern United States and eastern Canada, causing coarse woody debris (CWD) deposition in riparian areas and associated streams. During 1999 and 2000, tree canopy damage, stream physical habitat, and wood deposition were evaluated within 51 first-, second-, and third-order streams located within five eastern Adirondack Mountain watersheds (New York, U.S.A.). In first- through third-order streams, the number and volume of stream debris dams increased in response to streamside trees with canopy damage. Tree canopy damage was not a significant predictor for individual pieces of stream CWD but was correlated with CWD >10 cm in diameter in third-order, but not first-order, streams. At debris dam locations, bankfull width was greater and stream substrates consisted of increased fines. Woody debris resulting from the 1998 ice storm was not associated with increased pool formation; instead, boulders and rocky substrate were the dominant pool-forming elements. CWD length in first-order streams generally exceeded bankfull width, but in third-order streams, CWD length was shorter than bankfull width and therefore was subject to greater transport and accumulation into debris dams. Our results indicate that ice storm disturbances can increase wood inputs to first- through third-order forested stream ecosystems.

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 score0.214
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.200
Teacher spread0.184 · 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

Citations37
Published2002
Admission routes2
Has abstractyes

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