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Record W2141304467 · doi:10.1139/x08-068

Dynamics of wood in stream networks of the western Cascades Range, Oregon

2008· article· en· W2141304467 on OpenAlexvenueno aff
N. M. Czarnomski, D. M. Dreher, Kai U. Snyder, Julia Jones, Frederick J. Swanson

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
Fundersnot available
KeywordsSTREAMSWatershedEnvironmental scienceLandformHydrology (agriculture)DebrisForest managementLarge woody debrisCoarse woody debrisFlood mythFluvialGeologyHabitatEcologyGeographyRiparian zoneAgroforestryGeomorphology

Abstract

fetched live from OpenAlex

We develop and test a conceptual model of wood dynamics in stream networks that considers legacies of forest management practices, floods, and debris flows. We combine an observational study of wood in 25 km of 2nd- through 5th-order streams in a steep, forested watershed of the western Cascade Range of Oregon with whole-network studies of forest cutting, roads, and geomorphic processes over the preceding 50 years. Statistical and simple mass balance analyses show that natural process and forest management effects on wood input, transport processes, and decomposition account for observed patterns of wood in the stream network. Forest practices reduced wood amounts throughout the network; in headwater streams these effects are fixed in stream segments bordered by cuts and roads, but in larger channels they are diffused along the channel by fluvial transport of wood. Landforms and roads limited delivery of wood by debris flows to mainstem channels. Network dynamics studies and watershed management plans should include spatial patterns of debris flow initiation and runout, flood redistribution, and reduction of wood in the network by forest cutting and intentional wood removal from channels on time scales of forest succession and recurrence of major floods.

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)0.001
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 score0.698
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.266
Teacher spread0.241 · 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

Citations30
Published2008
Admission routes1
Has abstractyes

Explore more

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