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Record W2116812336 · doi:10.1139/cjfr-2015-0138

Influence of hydrologic modifications on <i>Fraxinus pennsylvanica</i> in the Mississippi River Alluvial Valley, USA

2015· article· en· W2116812336 on OpenAlexvenueno aff
Hugo K.W. Gee, Sammy L. King, Richard F. Keim

Bibliographic record

VenueCanadian Journal of Forest Research · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsHydrology (agriculture)LeveeEnvironmental scienceFloodplainFlood mythOverbankAlluviumFlooding (psychology)EcologyGeographyGeologyBiologyGeomorphology

Abstract

fetched live from OpenAlex

We used tree-ring analysis to examine the radial growth response of a common, moderately flood-tolerant species (Fraxinus pennsylvanica Marsh.) to hydrologic and climatic variability for &gt;40 years before and after hydrologic modifications affecting two forest stands in the Mississippi River Alluvial Valley (USA): a stand without levees below dams and a stand within a ring levee. At the stand without levees below dams, spring flood stages decreased and overall growth increased after dam construction, which we attribute to a reduction in flood stress. At the stand within a ring levee, growth responded to the elimination of overbank flooding by shifting from being positively correlated with river stage to not being correlated with river stage. In general, growth in swales was positively correlated with river stage and Palmer Drought Severity Index (an index of soil moisture) for longer periods than flats. Growth decreased after levee construction, but swales were less impacted than flats likely because of differences in elevation and soils provided higher soil moisture. Results of this study indicate that broadscale hydrologic processes differ in their effects on the flood regime, and the effects on growth of moderately flood-tolerant species such as F. pennsylvanica can be mediated by local-scale factors such as topographic position, which affects soil moisture.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.474
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.112
GPT teacher head0.321
Teacher spread0.209 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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