MétaCan
Menu
Back to cohort
Record W170910119 · doi:10.5822/978-1-61091-203-7_6

Boreal Forest, Canada

2012· book-chapter· en· W170910119 on OpenAlexaffabout
Meg A. Krawchuk, Kim Lisgo, Shawn Leroux, Pierre Vernier, Steve Cumming, Fiona K. A. Schmiegelow

Bibliographic record

VenueIsland Press/Center for Resource Economics eBooks · 2012
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversité LavalUniversity of OttawaUniversity of Alberta
Fundersnot available
KeywordsBorealTaigaClimate changeGeographyEcosystemEnvironmental scienceEcologyEnvironmental resource managementForestryBiology

Abstract

fetched live from OpenAlex

The vast expanse of the boreal forest in Canada is home to a diversity of wide-ranging animals, from migratory land birds and waterfowl to the largest caribou herds in the world. Boreal ecosystems are likely to experience dramatic changes in this century, particularly through anticipated alteration in vegetation and wildfire regimes as a result of the greater-than-average rate of warming predicted for more northerly latitudes. Conservation efforts aimed at addressing the challenge of climate change are focused on finding win-win strategies that accomplish both mitigation and adaptation by protecting the carbon storage potential of boreal ecosystems and developing innovative tools for integrating the effects of economic land uses, natural ecosystem dynamics, and climate change into a unified approach to conservation planning in a multiuse, yet largely intact, landscape. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.105
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0510.011

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 head0.009
GPT teacher head0.173
Teacher spread0.164 · 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

Citations4
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueIsland Press/Center for Resource Economics eBooksSame topicFire effects on ecosystemsFrench-language works237,207