MétaCan
Menu
Back to cohort
Record W4383262632 · doi:10.1080/11956860.2023.2231262

No evidence of a northward biome shift of treeline in the Mackay Lake region, north-central Canada

2023· article· en· W4383262632 on OpenAlexvenueaboutno aff
Kevin P. Timoney

Bibliographic record

VenueEcoscience · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsTundraBiomeSubarctic climateTaigaBlack spruceEcologyVegetation (pathology)Physical geographyClimate changeBorealGeographyDeforestation (computer science)ArcticEcosystemBiology

Abstract

fetched live from OpenAlex

Major changes in boreal and subarctic climates have occurred in recent decades, but uncertainty exists as to how biota are responding, e.g., whether the subarctic forest-tundra (‘treeline’) will advance northward. Ground-based vegetation data from the Mackay Lake region of the Northwest Territories were used to determine if there is evidence of a northward biome shift. Near the treeline, current plant communities are similar to those documented 40–67 years ago. There was little or no evidence of recent tree regeneration by seed, expansion of tree stands, or colonization by trees of adjacent tundra. Black spruce stems require ~75 years to reach 2.5 m in height and be discernible as trees on photography and satellite imagery. Branches 20–30 years old are the preferred age for initiation of an upright stem from layering. Upright stems of clonal spruce within the forest-tundra may be far younger than their near-ground or belowground stems. Whether northward biome migration will occur will depend on the interplay between climatic, landscape, and soil factors, species migration capacities, wildfires, and whether afforestation outpaces deforestation. In the forest-tundra, climatic change may not result in significant biome shifts, but rather in changes in the relative abundance of species already present.

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 score0.387
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.057
GPT teacher head0.243
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 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

Citations9
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueEcoscienceSame topicClimate change and permafrostFrench-language works237,207