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Record W4404734186 · doi:10.1016/j.heliyon.2024.e40689

Retraction notice to “A deep learning approach based on graphs to detect plantation lines” [Heliyon Volume 10, Issue 11, 15 June 2024, e31730]

2024· retraction· en· W4404734186 on OpenAlexaff
Diogo Nunes Gonçalves, José Marcato, Mauro dos Santos de Arruda, Vanessa Jordão Marcato Fernandes, Ana Paula Marques Ramos, Danielle Elis Garcia Furuya, Lucas Prado Osco, Hongjie He, Lúcio André de Castro Jorge, Jonathan Li, Farid Melgani, Hemerson Pistori, Wesley Nunes Gonçalves

Post-publication record

OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.

Bibliographic record

VenueHeliyon · 2024
Typeretraction
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNoticeArtificial intelligenceDeep learningMachine learningComputer scienceData sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

[This retracts the article DOI: 10.1016/j.heliyon.2024.e31730.].

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)0.004
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.284

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.035
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0850.063

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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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