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Evidence for the widespread occurrence of ancient forests on cliffs

2000· article· en· W1990305648 on OpenAlexafffundabout
Douglas W. Larson, Uta Matthes, John A. Gerrath, Nathan Larson, Jean M. Gerrath, Jeffrey C. Nekola, Gary L. Walker, Stefan Porembski, Andrew Charlton

Bibliographic record

VenueJournal of Biogeography · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCliffEscarpmentTemperate climateCanopyGeographyTemperate rainforestHabitatLianaOld-growth forestEcologyBiologyForestryArchaeologyEcosystem

Abstract

fetched live from OpenAlex

Abstract Aim The objective of this work was to determine if the existence of ancient forests on cliffs was specific to the Niagara Escarpment, Canada, or part of a globally widespread pattern. Location Sixty‐five cliff sites were visited in five countries in the temperate climatic zone, and trees were sampled for age and growth rate on forty‐six of these. Methods Two hundred and twenty‐four core samples or cross‐sections were taken from trees on cliffs that varied in height, aspect, rock‐type, and exposure. General observations were also made of regeneration of the tree species forming the mature canopy, and other habitat conditions. Results The evidence shows that ancient slow‐growing forest occurs on most cliffs. Age and growth rate distributions were similar at all treed sites. Small‐statured Thuja, Juniperus , or Taxus stems with age estimates in excess of 1000 years were found in the United States, the United Kingdom and France, and small Pinus and Quercus stems nearly 400 years in Germany. There was a high rate of recurrence of plants in the genera Polypodium , Asplenium , Cystopteris , Campanula , Rosa , Prunus , Hedera , and Sorbus . Most of the sites appear to be habitats of completely natural origin. Conclusions We conclude that ancient natural forest is a normal feature of cliffs, at least in the temperate zone.

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 score0.131
Threshold uncertainty score0.186

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 communication0.0000.000
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 head0.060
GPT teacher head0.263
Teacher spread0.203 · 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

Citations48
Published2000
Admission routes3
Has abstractyes

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