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Record W2147708615 · doi:10.7202/004838ar

Development of a model for estimating the sensitivity of Canadian peatlands to climate warning

2002· article· en· W2147708615 on OpenAlexaffvenueabout
I M Kettles, C. Tarnocai

Bibliographic record

VenueGéographie physique et Quaternaire · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité de MontréalGeological Survey of Canada
Fundersnot available
KeywordsPeatPermafrostEnvironmental scienceGreenhouse gasSoil waterClimate changeClimate modelSoil carbonVegetation (pathology)Global warmingClimate sensitivityClimatologyAtmospheric sciencesPhysical geographySoil scienceGeologyEcologyGeographyOceanography

Abstract

fetched live from OpenAlex

Under current scenarios of increasing greenhouse gases, the expected increases in global temperatures have the potential to affect, in many areas, the peat- lands that now cover 14 % of the soil area of Canada. A model for estimating peatland sensitivity to climate warming was developed using published information on the current state of climate, vegetation, and permafrost together with the changes expected with a doubling of CO 2 . Calculations based on this sensitivity model and data for the areal extent and carbon content of organic soils in Canada, show that approximately 60 % of the area of Canadian peatlands is expected to be severely to extremely severely affected by climate warming. These peatlands, which are deemed most sensitive to climate warming, also contain 53 % of the 154 Gt of carbon found in organic soils.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.249
Teacher spread0.221 · 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 designSimulation or modeling
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

Citations35
Published2002
Admission routes3
Has abstractyes

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