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Record W2527939907 · doi:10.1139/cjb-2016-0182

The 8200-year vegetation history of an urban woodland as reconstructed from pollen and plant remains

2016· article· en· W2527939907 on OpenAlexaffvenueabout
Martin Lavoie, Pierre J. H. Richard

Bibliographic record

VenueBotany · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalUniversité LavalCenter for Northern Studies
Fundersnot available
KeywordsMacrofossilTsugaTiliaWoodlandPollenVegetation (pathology)MapleEcologyBiologyGeographyForestry

Abstract

fetched live from OpenAlex

Data on the long term evolution of urban forests are rare. Using pollen and macrofossil analyses of a sediment core collected in a swampy forest hollow on Île aux Chats, an island in Bois-de-Saraguay woodland park in Montreal (Quebec), the postglacial history of a maple forest was reconstructed at a local spatial scale for the last 8200 years. Results show that after Île aux Chats emerged from the waters of Lake Lampsilis, it was rapidly colonized by a maple forest that was already diversified 8000 years ago. More than half of the vascular species identified in the macrofossil assemblages are absent from the local plant community today, notably coniferous species (Pinus resinosa, Larix laricina, Picea mariana). Other plants have multiplied their populations over time (Tilia americana, Tsuga canadensis, Acer rubrum). The maple forest was probably sustained by a small-scale gap dynamic caused by windthrow, fires having apparently been very rare. Fagus grandifolia, never abundant locally in the past, is observed to be currently expanding, and could eventually compete with Acer saccharum. This study constitutes not only a rare plurimillenial study of an urban woodland in eastern North America, but also of a maple forest at a local spatial scale.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.246
Threshold uncertainty score0.489

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.206
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2016
Admission routes3
Has abstractyes

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