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Record W1819590897 · doi:10.1002/jqs.2793

Pine stumps in Irish peats: is their occurrence a valid proxy climate indicator?

2015· article· en· W1819590897 on OpenAlexaff
A H McGeever, Fraser Mitchell

Bibliographic record

VenueJournal of Quaternary Science · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsTrinity College
Fundersnot available
KeywordsBogHolocenePeatPhysical geographyClimate changePinus <genus>GeologyPaleoclimatologyRadiocarbon datingEnvironmental scienceProxy (statistics)EcologyClimatologyGeographyArchaeologyOceanographyPaleontology

Abstract

fetched live from OpenAlex

ABSTRACT We investigate the temporal and spatial distribution of pine ( Pinus sylvestris ) stumps preserved in peat deposits to test whether their occurrence can be used as an indicator of climatic shifts to drier conditions. Radiocarbon dates of sub‐fossil stumps were collected from the literature, along with environmental data throughout the island of Ireland. Data were analysed using non‐parametric statistical techniques. There was no distinct geographical pattern observed in the distribution of pine stumps on bog surfaces. Tree ages ranged from 66 to 500 years with 85.7 % of these &lt;300 years. Pines occurred on bogs from ca. 8500 to 500 cal a BP. The temporal distribution during the Holocene was characterized as a mid‐Holocene peak in sites supporting pine, with two gaps either side of this peak. Our current understanding of past climate dynamics failed to explain this temporal distribution. The onset, mid‐Holocene peak and cessation of bog sites supporting the presence of bog pines appears to be driven by changes in pine seed and bog surface area availability during the Holocene rather than changes in climate. We conclude that variability in the occurrence of Irish bog pines is not a valid climate proxy as factors other than climate influence their presence and thereby disrupt the climate signals.

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)0.005
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 score0.007
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.072
GPT teacher head0.326
Teacher spread0.254 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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