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Perception of ecosystem services by peri-urban farmers in São Paulo, SP, Brazil

2021· article· en· W4404226380 on OpenAlexaboutno aff
Diego Maciel Blum da Silva, Clóvis José Fernandes de Oliveira

Bibliographic record

VenueGaia Scientia · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsPeriEcosystem servicesPerceptionEcosystemGeographyEnvironmental resource managementEnvironmental planningBusinessPsychologyEnvironmental scienceMedicineEcologyBiology

Abstract

fetched live from OpenAlex

The managements adopted in agroecosystems may interfere positively or negatively in the different ecosystem services (ES). Understanding the perception of ES and its importance is very relevant for family farming public policies, in accordance with practices that can mitigate the effects of climate change and create resilience for agroecosystems, with environmental gains for the entire society. With increased understanding in this sense, it is possible to improve the management of the agroecosystem and increase the degree of positivity of interferences in the ES. The objective of this work is to analyze the perception of ES by farmers located in peri-urban areas. The study was carried out in the Comuna da Terra Irmã Alberta, a pre-settlement established in the capital of São Paulo State, with participative observation and application of semi-structured interviews with key informants. The most perceived ES were those related to ecosystem support functions and culture, the most mentioned being: “nursery”, “cultural identity”, “food”, “sound regulation”, “aesthetic appreciation” and “air quality”. Sociocultural aspects and the spatial context in which they are inserted influenced the perception of ecosystem services. The presence of agroforestry backyards was the most prominent factor influencing the perception of SE.

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)0.000
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 score0.670
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.200
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

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

Citations0
Published2021
Admission routes1
Has abstractyes

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