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Record W2123812251 · doi:10.2980/21-2-3708

Frequency and factors of earlywood frost ring formation in jack pine ( <i>Pinus banksiana</i> ) across northern lower Michigan

2014· article· en· W2123812251 on OpenAlexvenueno aff
Kathryn R. Kidd, Carolyn A. Copenheaver, Audrey Zink-Sharp

Bibliographic record

VenueEcoscience · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
FundersMichigan Department of Natural ResourcesU.S. Department of Agriculture
KeywordsFrost (temperature)Abiotic componentCambiumGrowing seasonDendrochronologyPinus <genus>Environmental scienceBiologyBotanyEcologyPhysical geographyGeologyXylemGeographyGeomorphologyPaleontology

Abstract

fetched live from OpenAlex

Late spring frost disturbances have significant ecological and physiological impacts on forests. Frost-induced cambial damage that occurs when cells are actively dividing can result in the formation of frost rings, abnormal modifications to wood anatomy within the annual growth rings of an injured tree. Frost rings are indicators of growing season frost damage to the cambium and therefore have potential to be used both as a proxy in the reconstruction of extreme climatic events and to identify frost-prone environmental conditions. In this study, we measured the occurrence of earlywood frost rings across cambial age and diameter class in 11 jack pine (Pinus banksiana) populations of northern lower Michigan. Earlywood frost ring formation was greater in younger trees and in trees with smaller diameters. Biotic (cambial age, diameter, and ring width) and abiotic (elevation, initial site-related growth rate, and minimum temperature) factors demonstrated significant influence on the probability of earlywood frost ring formation. When using frost rings as a proxy of historical climate, susceptibility to abrupt freezing temperatures during the growing season and thus the ability of an individual tree to record a frost disturbance should be considered.

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 score0.108
Threshold uncertainty score0.906

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 communication0.0000.001
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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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

Citations19
Published2014
Admission routes1
Has abstractyes

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