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Record W2157139192

Trends in Quercus Macrocarpa Vessel Areas and their Implications for Tree-Ring Paleoflood Studies

2002· article· en· W2157139192 on OpenAlexaboutno aff
Scott St. George, Erik Nielsen, Jacques Tardif

Bibliographic record

VenueUA Campus Repository (The University of Arizona) · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
Fundersnot available
KeywordsFlood mythDendrochronologyFlooding (psychology)GeologyPhysical geographyHydrology (agriculture)GeographyPaleontologyArchaeologyGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

Changes in mean earlywood vessel areas in mature Quercus macrocarpa were analyzed to determine possible sources of bias in paleoflood records derived from anatomical tree-ring signatures. Tree-ring cores were collected at intervals along the vertical axis of four Q. macrocarpa in a flood-prone stand near the Red River in Manitoba. The WinCELL PRO image analysis system was used to measure mean vessel areas in each annual ring. Most cores displayed a pronounced juvenile increase in mean vessel area before stabilizing between 40 and 60 years. The lowest samples from several trees contain rings with anomalously small mean vessel areas that are coincident with high-magnitude Red River floods in 1950 and 1997. The anatomical response of Q. macrocarpa appears to be conditional on the relative timing of earlywood development and flooding. Flood signatures are most strongly developed near the tree base and become less evident up the trunk. Most signatures disappear between one and three meters in height. Differences in flood response between trees are likely caused by internal differences rather than hydrological or topographic factors. Paleoflood studies based on samples obtained exclusively at breast height may miss some anatomical flood signatures and underestimate flood frequency relative to earlier intervals.

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 score0.066
Threshold uncertainty score0.353

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)0.0000.000

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 head0.015
GPT teacher head0.206
Teacher spread0.191 · 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

Citations59
Published2002
Admission routes1
Has abstractyes

Explore more

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