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Record W1787466138 · doi:10.1002/esp.2183

Tree establishment on bars in low‐order gravel‐bed mountain streams

2011· article· en· W1787466138 on OpenAlexafffund
Isabelle Charron, Edward A. Johnson, Y. E. Martin

Bibliographic record

VenueEarth Surface Processes and Landforms · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsConcordia UniversityUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSTREAMSGeologyDeposition (geology)SedimentChannel (broadcasting)AlluviumHydrology (agriculture)ErosionAccretion (finance)Alluvial fanGeomorphologyGeotechnical engineering

Abstract

fetched live from OpenAlex

ABSTRACT In many large alluvial rivers, trees often recruit and survive along laterally accreted sediments on bars. This produces a gradient of tree ages and composition with distance from the active channel. However, in low‐order, gravel‐bed mountain streams, such as the stream investigated in this study, it is suggested that vertical accretion results in sediment deposition patterns on bars that are often highly patchy. Consequently, tree species and ages are also heterogeneously distributed, rather than having distinct linear or arcuate banding patterns with distance from the channel. In addition, overall age patterns of trees on these bars follow the distribution of floods, with numerous young trees and few older trees. Recruitment is fairly continuous on these bars and is not correlated with high water years, suggesting that even flows close to bankfull levels are capable of transporting fine sediment to the bars on which trees establish. This pattern of sediment deposition/erosion and the resulting tree recruitment and survival seem to be a result of valley confinement and the lack of lateral accretion in these smaller, mountainous channels. Copyright © 2011 John Wiley & Sons, Ltd.

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 score0.036
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.199
Teacher spread0.189 · 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

Citations34
Published2011
Admission routes2
Has abstractyes

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