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Record W1981841167 · doi:10.3959/1536-1098-69.2.81

Signal Strength In Sub-Annual Tree-Ring Chronologies from<i>Pinus ponderosa</i>In Northern New Mexico

2013· article· en· W1981841167 on OpenAlexaff
Becky Brice, Kara K. Lorion, Daniel Griffin, Alison K. Macalady, Christopher H. Guiterman, James H. Speer‬, Laura R. Benakoun, Amy Cutter, Megan E. Hart, Michael P. Murray, Stephen E. Nash, Robert Shepard, Alexander K. Stewart, Haitao Wang

Bibliographic record

VenueTree-Ring Research · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsCanadian Forest ServiceUniversity of Guelph
FundersU.S. Department of EnergyNational Park ServiceNational Science Foundation
KeywordsDendrochronologyChronologyPinus <genus>DendroclimatologySignal strengthPhysical geographyStatisticGeographyClimatologyGeologyStatisticsArchaeologyComputer scienceMathematicsBiology

Abstract

fetched live from OpenAlex

The creation of chronologies from intra-annual features in tree rings is increasingly utilized in dendrochronology to create season-specific climate histories, among other applications. A conifer latewood-width network has recently been developed for the southwestern United States, but considerable uncertainty remains in understanding site and species differences in signal strength and sample depth requirements. As part of the 22nd annual North American Dendroecological Fieldweek, the first Pinus ponderosa earlywood-width (EW) and latewood-width (LW) chronologies were developed for the Jemez Mountains in northern New Mexico. The aim was to extend an existing total ring-width (TW) chronology and to assess the potential for creating long LW chronologies. Analysis of chronology signal strength suggests that large sample size requirements remain a considerable hurdle for creating P. ponderosa LW chronologies longer than 400 years. At the Cat Mesa site, twenty-three sample trees were required to capture a statistically acceptable common signal in adjusted latewood (LWa), whereas only four samples were required for EW. This is significantly higher than sample depth requirements for LWa from the few other chronologies in the region where this statistic has been reported. A future priority should be to develop a conceptual guide for site and tree selection in order to maximize the potential for enhancing LW signal and for creating a robust network of multi-century LW chronologies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.004

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 head0.049
GPT teacher head0.295
Teacher spread0.245 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2013
Admission routes1
Has abstractyes

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