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Record W2892390660 · doi:10.1139/er-2018-0065

Demand for nonprovisioning ecosystem services as a driver of change in the Canadian boreal zone<sup>1</sup>

2018· article· en· W2892390660 on OpenAlexaffvenueabout
Karl A. Lamothe, Haibin Dong, Oscar E. Senar, Sonja Teichert, Irena F. Creed, David P. Kreutzweiser, Fiona K. A. Schmiegelow, Lisa Venier

Bibliographic record

VenueEnvironmental Reviews · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsUniversity of TorontoCanadian Forest ServiceUniversity of AlbertaNatural Resources CanadaUniversity of SaskatchewanWestern University
Fundersnot available
KeywordsEcosystem servicesBorealSustainabilityEcosystemNatural resourceClimate changeEnvironmental resource managementProvisioningNatural resource economicsBusinessGeographyEcologyEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

The Canadian boreal zone provides ecosystem services from local to global scales. Either directly or indirectly, demands for these services have and will continue to serve as drivers of change in the region. Here we present evidence for past, present, and potential future demand for maintaining nonprovisioning ecosystem services (NPrES), defined as indirect and nonmarketable services obtained from ecosystems as a driver of change in the boreal zone. Our evidence of demand stems from federal and provincial policies, actions by Indigenous peoples, and nongovernmental initiatives that aim to maintain the sustainability of natural resource extraction and ecosystem condition of the boreal. Presently, the demand for NPrES influences decisions related to natural resource development (e.g., forestry) that in turn impacts the condition of the boreal zone. Informed by the present conditions and past trends, three future scenarios to the year 2050 are presented that contrast in their trajectory—status quo, increased demand for NPrES, and decreased demand for NPrES. We also summarize the interactions among other drivers of change in the boreal and the synergies and trade-offs among the different types of demand for NPrES. Ultimately, sustainability of the boreal zone and the ecosystem services it provides will result from a complex suite of interacting drivers of change, where the balance of demands for provisioning and NPrES will continue to influence regional conditions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.022
GPT teacher head0.248
Teacher spread0.227 · 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.

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

Citations28
Published2018
Admission routes3
Has abstractyes

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