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Record W2143428867 · doi:10.1177/0959683614544054

Vegetation dynamics in relation to late Holocene climate variability and disturbance, Outaouais, Québec, Canada

2014· article· en· W2143428867 on OpenAlexafffundabout
Karelle K.L.B. Lafontaine-Boyer, Konrad Gajewski

Bibliographic record

VenueThe Holocene · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsTsugaPollenHoloceneEcological successionPalynologyPhysical geographyVegetation (pathology)EcologyClimate changeMacrofossilVarveGeologyChronologyDeciduousDisturbance (geology)ClimatologyGeographyOceanographyPaleontologyBiologySediment

Abstract

fetched live from OpenAlex

A pollen diagram from Lac Brulé in southwestern Québec (45°43′09″N, 75°26′32″W, 270 m) provides a late Holocene history of the vegetation. The presence of varved sediments permitted the development of a high-resolution (10-year), cross-dated chronology with an estimated error of approximately 1%. During the last 1400 years, the forests were dominated by Tsuga, Fagus, Betula, Acer and Pinus. A peak in microcharcoal and evidence of post-fire succession suggest that the changes in the pollen assemblages around ad 1375 were a consequence of a fire in the region. There was a decrease in pollen influx of several deciduous taxa and Tsuga between ad 1600 and 1700, suggesting a rapid climate change that was significant enough to have affected pollen production of these taxa. This change, associated with the beginning of the ‘Little Ice Age’ in the region, affected the forest composition for the subsequent centuries. A detailed comparison of this pollen record with that from a nearby pollen diagram also prepared at high-temporal resolution shows the ability of pollen records to record short-period climate variations and disturbances.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.661

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 studies0.0000.000
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 head0.008
GPT teacher head0.210
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

Citations25
Published2014
Admission routes3
Has abstractyes

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