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Record W2916035204 · doi:10.5751/es-10349-240117

Toward understanding the dynamics of land change in Latin America: potential utility of a resilience approach for building archetypes of land-systems change

2019· article· en· W2916035204 on OpenAlexvenueno aff
Juan Rocha, Matilda Baraibar Norberg, Lisa Deutsch, Ariane de Bremond, Jordan Sky Oestreicher, Florencia Rositano, Cecilia C. Gelabert

Bibliographic record

VenueEcology and Society · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Agricultural Systems Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsResilience (materials science)Environmental resource managementArchetypeClimate changeLand useLand use, land-use change and forestryEnvironmental changeLatin AmericansGlobal changeEnvironmental planningGeographyEnvironmental sciencePolitical scienceEcologyGeologyOceanography

Abstract

fetched live from OpenAlex

Rocha, J. C., M. Baraibar, L. Deutsch, A. de Bremond, J. Oestreicher, F. Rositano, and C. Gelabert. 2019. Toward understanding the dynamics of land change in Latin America: potential utility of a resilience approach for building archetypes of land-systems change. Ecology and Society 24(1):17. https://doi.org/10.5751/ES-10349-240117

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.003
Science and technology studies0.0010.007
Scholarly communication0.0040.012
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.035
GPT teacher head0.241
Teacher spread0.206 · 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 designTheoretical or conceptual
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

Citations23
Published2019
Admission routes1
Has abstractyes

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