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Record W2409542940 · doi:10.1139/cjfr-2016-0078

Frequency of sprout-origin trees in pre-European settlement forests of the southern Appalachian Mountains

2016· article· en· W2409542940 on OpenAlexvenueno aff
Carolyn A. Copenheaver, Tara L. Keyser

Bibliographic record

VenueCanadian Journal of Forest Research · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsDeciduousRidgeGeographyForestryTiliaEcologyBiologyPollenCartography

Abstract

fetched live from OpenAlex

We hypothesized that tree form, recorded in historical public land surveys, would provide a valuable proxy record of regeneration patterns during early-European settlement of North America’s eastern deciduous forest. To test this hypothesis, we tallied stem form from witness trees used in land survey records in the southern Appalachian Mountains from 13 counties spanning four physiographic provinces: Piedmont, Blue Ridge, Ridge and Valley, and Cumberland Plateau. A total of 3% of witness trees used in the land surveys were of sprout origin. American basswood (Tilia americana L.) exhibited the highest proportion of sprout-origin trees at 12%. Other overstory species with a high proportion of sprout-origin trees were hickory (Carya sp.), red maple (Acer rubrum L.), and sycamore (Platanus occidentalis L.), all with 6% of stems being from sprout origin. Blue Ridge had significantly more sprout-origin trees compared with the other three physiographic provinces. Forests in the southern Appalachian Mountains during the pre-European settlement period had a suite of disturbances that controlled their growth and regeneration; however, most of these disturbances did not result in large-scale tree mortality, and therefore, sprouts were not an important source of regeneration.

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)0.001
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 score0.862
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.292
Teacher spread0.249 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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