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Record W7034908150

A 2300-year reconstruction of environmental change from Parc national du Mont-Orford, southeastern Québec, using high-resolution pollen, charcoal and X-ray fluorescence records

2020· dissertation· en· W7034908150 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2020
Typedissertation
Languageen
FieldArts and Humanities
TopicHistorical Studies in Central America
Canadian institutionsnot available
Fundersnot available
KeywordsTsugaPollenClimate changeTemperate climateCharcoalTemperate rainforestPhenologyPaleoclimatologyEnvironmental changeBiome
DOInot available

Abstract

fetched live from OpenAlex

We used a high-resolution lacustrine pollen record from Étang Fer-de-Lance (45°21'21.9"N, 72°13'35.3"W), southern Québec, Canada, together with microcharcoal, to infer forest dynamics, climate and human impacts over the past 2300 years. The lake is located in the sugar maple-basswood domain of the Northern Temperate Forest. We found that Fagus grandifolia (American beech) and Tsuga canadensis (eastern hemlock) significantly declined over the past 700 years. Over the last millennium, Picea glauca (white spruce), Picea mariana/rubens (black and red spruce), and Pinus strobus (eastern white pine) significantly increased. Using the modern analog technique (MAT), we found a warm and dry first millennium AD, a somewhat less warm and less dry Medieval Climate Anomaly, and a cold and wet Little Ice Age. The signal for human modification of the landscape first appeared at ~AD 1550-1650 as increases in Ambrosia (ragweed) and Poaceae (grasses) from Indigenous agriculture. The signal of European settler landscape modification appeared at ~AD 1770 as the beginning of a steep, “classic” Ambrosia rise. It intensified over the subsequent 250 years as further increases in non-arboreal pollen taxa and early successional Acer (maple) species. Microcharcoal analysis showed that fire is a natural part of the sugar maple-basswood domain with a mean fire return interval of 515 years.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.229
Teacher spread0.187 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

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