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Record W2321777915 · doi:10.1139/cjes-2016-0004

Late-glacial and Holocene evolution as a driver of diversity and complexity of the northeastern North American alpine landscapes: a synthesis

2016· article· en· W2321777915 on OpenAlexaffvenue
Jean‐Philippe Martin, Daniel Germaın

Bibliographic record

VenueCanadian Journal of Earth Sciences · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsGlacial periodGeologyPhysical geographyDeglaciationHolocenePleistoceneEarth scienceClimate changeGeomorphologyPaleontologyGeographyOceanography

Abstract

fetched live from OpenAlex

Mid-altitude, mid-latitude mountains are complex environments owing to their Pleistocene glacial heritage, the importance of geomorphic processes on the steep slopes, and the climatic conditions that are often close to periglacial. These factors, along with the fragmentation of the alpine habitats, enhance the topographic and floristic diversity of these environments in northeastern North America. Through case studies, this synthesis underlines the interactions between the geosphere (glacial, paraglacial, and periglacial processes), the atmosphere (climatic fluctuations), and the biosphere (vegetation establishment and evolution to the present day) that explain the low elevation of the northeastern North American alpine environment and that testify to its complexity. Vegetation established earlier in the southern ranges, following the same general trend as the Laurentian Ice Sheet recession. However, local factors such as ice retreat, response to global-scale climate changes, and paraglacial processes acted in synergy to increase the resilience and to influence the occurrence of alpine landscapes. The establishment of the latter environment can therefore be considered to be azonal. Finally, our findings highlight the lack of a conceptual framework, systemic studies, and multi-proxy reconstructions of alpine environments located at the limit of bioclimatic zones controlled by the equilibrium between biostatic and rhexistatic regimes.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.818
Threshold uncertainty score0.362

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.219
Teacher spread0.186 · 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

Citations11
Published2016
Admission routes2
Has abstractyes

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