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Record W2460309324 · doi:10.1002/jqs.2875

A 9600‐year record of water table depth, vegetation and fire inferred from a raised peat bog, Prince Edward Island, Canadian Maritimes

2016· article· en· W2460309324 on OpenAlexafffundabout
Matthew Peros, Kathleen L. Chan, Gabriel Magnan, Leila Ponsford, James Carroll, Terry McCloskey

Bibliographic record

VenueJournal of Quaternary Science · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversité du Québec à MontréalBishop's University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBogOmbrotrophicPeatMacrofossilTestate amoebaeSphagnumGeologyMireVegetation (pathology)Physical geographyWater tableHoloceneHydrology (agriculture)OceanographyGeographyArchaeologyGroundwater

Abstract

fetched live from OpenAlex

ABSTRACT A 582‐cm‐long peat core was collected from Baltic Bog, an ombrotrophic peatland in north‐eastern Prince Edward Island, Canada. The core was studied for testate amoebae, plant macrofossils, macrocharcoal, peat humification and organic matter content. The results show that Baltic Bog first developed ∼9600 cal a BP as a minerotrophic peatland (fen) dominated by Cyperaceae. At 8200 cal a BP, the fen transitioned into a peat bog dominated by Sphagnum . Between 8200 and 4000 cal a BP, water table depth (WTD) was generally low and the bog surface supported trees such as Picea mariana . From 4000 to 1700 cal a BP, WTD rose and the bog became more open. The macrocharcoal results show that the period ∼2000–1000 cal a BP was characterized by several fire events that may have occurred on the bog surface at the core site. The results presented in this paper correspond closely with previous fossil pollen research done at Baltic Bog and suggest that regional climate change was a key factor in controlling long‐term WTD variability and vegetation change at the site.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.015
GPT teacher head0.236
Teacher spread0.221 · 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.

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
Published2016
Admission routes3
Has abstractyes

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