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Record W2594626417 · doi:10.1016/j.cosust.2017.01.015

Land system science in Latin America: challenges and perspectives

2017· article· en· W2594626417 on OpenAlexaff
Sébastien Boillat, Fabiano M. Scarpa, James P. Robson, Ignácio Gasparri, T. Mitchell Aide, Ana Paula Aguiar, Liana O. Anderson, Mateus Batistella, Marisa Gesteira Fonseca, Célia Futemma, H. Ricardo Grau, Sarah-Lan Mathez-Stiefel, Jean Paul Metzger, Jean Pierre Ometto, Marcos Antônio Pedlowski, Stephen G. Perz, Valentina Robiglio, Luciana Soler, Ima Célia Guimarães Vieira, Eduardo S. Brondízio

Bibliographic record

VenueCurrent Opinion in Environmental Sustainability · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsConceptualizationLivelihoodLand useContext (archaeology)StakeholderEnvironmental resource managementBiomeEnvironmental planningLand coverGeographyAgriculturePolitical scienceEcologyEcosystemEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

This article reviews the current status, trends and challenges of land system science in Latin America. We highlight the advances in the conceptualization, analysis and monitoring of land systems. These advances shift from a focus on the relationships between forests and other land uses to include a greater diversity of land cover and land-use types and the processes and interactions that link them. We then provide a biome-level typology of social-ecological land systems (SELS) as an approach to help connect local-level realities to regional processes and we discuss how this approach can help to design more socially inclusive land systems. We also discuss the increased role of distant socio-economic and ecological interactions that connect these SELS to global processes. Combined, these insights support a research agenda for land system science in the region that can develop more accurate and integrative monitoring of land change and their social and ecological consequences, better understand different stakeholder perspectives within a context of livelihood diversification, and encourage institutional feedbacks to govern land systems influenced by distant drivers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.763

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.038
GPT teacher head0.271
Teacher spread0.234 · 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

Citations66
Published2017
Admission routes1
Has abstractyes

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