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Parque Nacional Serra da Canastra (Minas Gerais - Brasil): proposta de painel interpretativo

2020· article· pt· W3008104418 on OpenAlexaff
Lílian Carla Moreira Bento, Thallita Isabela Silva Martins Nazar

Bibliographic record

VenueCaderno de Geografia · 2020
Typearticle
Languagept
FieldEnvironmental Science
TopicEnvironmental Sustainability and Education
Canadian institutionsLibrary of Parliament
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

O Parque Nacional Serra da Canastra é uma unidade de conservação integral localizada no sudeste do Estado de Minas Gerais, Brasil. Apesar desse tipo de unidade prever a visitação, não existe na área de estudo nenhum plano de interpretação ambiental que garanta aos visitantes o entendimento dos locais. Diante disso, o objetivo deste trabalho foi propor um painel interpretativo para a Cachoeira Casca D’Anta, visto ser um dos atrativos mais visitados e, portanto, ter maior potencial de utilização. O painel proposto foi elaborado a partir de um plano interpretativo que buscou responder alguns questionamentos norteadores, a saber: i- o que interpretar? (temática a ser abordada), ii- por que interpretar?, iii- público-alvo, iv- como? (justificativa para o tipo de meio interpretativo escolhido) e v- onde (localização do meio selecionado). O painel apresentado visa preencher uma lacuna do ponto de vista do entendimento, no que diz respeito a origem da cachoeira de Casca D’anta. Relevante destacar que se trata apenas de uma proposta e que sua implantação dependerá de recursos da gerência do parque.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.127
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0070.017
Scholarly communication0.0100.008
Open science0.0020.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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 designNot applicable
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

Citations2
Published2020
Admission routes1
Has abstractyes

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