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Record W2185582219 · doi:10.2136/sssaspecpub57.2ed.c4

Influence of Climate and Land Use Change on Carbon in Agriculture, Forest, and Peatland Ecosystems across Canada

2009· book-chapter· en· W2185582219 on OpenAlex

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSSSA special publication series · 2009
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsNatural Resources CanadaAgriculture and Agri-Food CanadaCanadian Forest Service
Fundersnot available
KeywordsEnvironmental scienceClimate changeEcosystemPeatWetlandLand useLand use, land-use change and forestryAgroforestryEcology

Abstract

fetched live from OpenAlex

This chapter illustrates the influence of climate change on the carbon (C) pools in managed ecosystems across Canada. Specifically, the ecosystems considered are agricultural environments, forests, and peatlands. Much of the focus of discussion is on the interaction between different C pools, under changing climatic conditions, with specific reference to C fluxes in the three managed ecosystems. The need for the intense management of Canadian peatlands–wetlands, forests, and agricultural ecosystems is increasing to fulfill the requirements of food, livestock feed, fiber, and fuel production. Climate change affects both the distribution and character of the landscape through changes in temperature, precipitation, and natural disturbance patterns. These impacts are not entirely separable from the effects of other anthropogenic changes such as land use change, soil degradation, erosion, and drainage, all of which may be exacerbated by climate change.

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)0.000
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 score0.823
Threshold uncertainty score0.864

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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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