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Record W3169797936 · doi:10.5418/ra2013.0912.0005

GEOPROCESSAMENTO APLICADO À ANÁLISE DA FRAGILIDADE AMBIENTAL

2013· article· gl· W3169797936 on OpenAlex
Elias Rodrigues da Cunha, Vitor Matheus Bacani, Lucy Ribeiro Ayach

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.

Bibliographic record

VenueRevista da ANPEGE · 2013
Typearticle
Languagegl
FieldEnvironmental Science
TopicGeography and Environmental Studies
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsMathematics

Abstract

fetched live from OpenAlex

The aim of this study aimed to analyze the environmental fragility analysis of a watershed Indai stream-MS, using geoprocessing techniques. The scientific technical procedures employed consisted in the application of the proposed methodology developed by The results showed four fragility levels: low, medium, high and very high, occupying 4.44%, 42.10%, 53% and 0.31% of the watershed area, respectively. It is concluded that of a watershed Indai stream presents a low prevalence and average degree of potential fragility, however when considering the anthropic factor has a high environmental fragility prevalence, indicating alertness regarding the intensification of the occupation, particularly in areas where installed the current rural settlements.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0320.022

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 head0.010
GPT teacher head0.210
Teacher spread0.200 · 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