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Record W2228608271 · doi:10.1111/jvs.12373

Pre‐industrial landscape composition patterns and post‐industrial changes at the temperate–boreal forest interface in western Quebec, Canada

2016· article· en· W2228608271 on OpenAlex
Victor Danneyrolles, Dominique Arseneault, Yves Bergeron

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Vegetation Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversité du Québec à RimouskiUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à MontréalNatural Sciences and Engineering Research Council of Canada
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaTembec
KeywordsAbies balsameaDisturbance (geology)Dominance (genetics)TaigaGeographyBorealTemperate climateBalsamEcological successionEcologyComposition (language)Temperate rainforestPhysical geographyForestryEnvironmental scienceEcosystemBiologyArchaeology

Abstract

fetched live from OpenAlex

Abstract Questions What were the pre‐industrial forest landscape composition patterns? Which factors had structured the pre‐industrial landscape patterns? How have pre‐industrial landscape patterns and post‐industrial disturbances controlled composition changes? Location An area of 4175 km 2 at the temperate–boreal forest interface of southwest Quebec, Canada. Methods Reconstruction of the pre‐industrial composition is based on an original early land survey data set (1874–1935). Composition changes were computed by comparing historical data with modern forest inventories. Landscape‐scale patterns and composition changes were assessed through spatially constrained clustering analysis. Results Pre‐industrial forest composition was structured across the landscape by the combination of environmental gradients (topography, deposits, drainage, etc.) and recurrence of fire. Frequency and intensity of fires were most likely the main drivers of forest dynamics and composition across the landscape. Black spruce ( Picea mariana ) and balsam fir ( Abies balsamea ) dominated hilly areas affected by former fires; aspen ( Populus tremuloides ) dominated lowlands following recent fire. White cedar ( Thuja occidentatlis ) and pines ( Pinus spp.) dominated areas probably affected by small surface fires. New disturbance regimes that were subsequently incurred by human activities have shifted the pre‐industrial landscape mosaic and have led to the current landscapes. Composition changes included a replacement of conifers by early successional species within settled or burned areas, and the maintenance of conifers and an increase in cedar dominance in areas affected by partial disturbance. Conclusions Post‐industrial composition changes must be perceived as complex interactions between pre‐industrial landscape patterns and natural and human disturbances. Such land‐use legacies could be important drivers of future landscape change and should be investigated and considered when predicting future climate‐induced ecological changes.

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.

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 score0.763
Threshold uncertainty score0.806

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 communication0.0000.001
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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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