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Record W4200113249 · doi:10.3390/land10121355

Topographic Variation in Forest Expansion Processes across a Mosaic Landscape in Western Canada

2021· article· en· W4200113249 on OpenAlexaffabout
Larissa Robinov, Chris Hopkinson, Mark C. Vanderwel

Bibliographic record

VenueLand · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of LethbridgeUniversity of Regina
Fundersnot available
KeywordsGrasslandTransectTree canopyCanopyGeographyAgroforestryGrazingForestryTaigaEcologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Changes to historic fire and grazing regimes have been associated with the expansion of tree cover at forest–grassland boundaries. We evaluated forest expansion across a mosaic landscape in western Canada using aerial photos, airborne laser scanning, and field transects. The annual rate of forest expansion (0.12%) was on the low end of rates documented across North America and was greater from the 1970s to the 1990s than from the 1990s to 2018. Most forest expansion occurred within 50 m of established forests, and 68% of all tree regeneration in grasslands was within 15 m of the forest edge. The intensity of cattle grazing did not affect the tree regeneration density. Despite the slow pace of land cover change, grassland areas near the forest edge had an average of 20% canopy cover and 9 m canopy height, indicating the presence of tall but sporadic trees. The rate of forest expansion, density of tree regeneration, and tree cover within grasslands were all greater at lower elevations where trembling aspen (Populus tremuloides) and white spruce (Picea glauca) were the dominant tree species. We conclude that proportions of forest–grassland cover on this landscape are not expected to change dramatically in the absence of major fire over the next several decades.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.256

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 studies0.0000.000
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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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