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Record W2900543321 · doi:10.1371/journal.pbio.2006686

Biodiversity assessments: Origin matters

2018· letter· en· W2900543321 on OpenAlexaff
Aníbal Pauchard, Laura A. Meyerson, Sven Bacher, Tim M. Blackburn, Giuseppe Brundu, Marc W. Cadotte, Franck Courchamp, Franz Essl, Piero Genovesi, Sylvia Haider, Nick D. Holmes, Philip E. Hulme, Jonathan M. Jeschke, Julie L. Lockwood, Ana Novoa, Martín A. Núñez, Duane A. Peltzer, Petr Pyšek, David M. Richardson, Daniel Simberloff, Kevin G. Smith, Brian W. van Wilgen, Montserrat Vilà, John R. Wilson, Marten Winter, Rafael Dudeque Zenni

Bibliographic record

VenuePLoS Biology · 2018
Typeletter
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersAgence Nationale de la Recherche
KeywordsBiologyBiodiversityCitationConservation biologyEcologyLibrary science

Abstract

fetched live from OpenAlex

Recent global efforts in biodiversity accounting, such as those undertaken through the Convention on Biological Diversity (CBD) and Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), are vital if we are to track conservation progress, ensure that we can address the challenges of global change, and develop powerful and scientifically sound indicators. Schlaepfer We strongly disagree with the approach of combining counts of native and non-native species because this will reduce our capacity to detect the effects of non-native species on native biodiversity with potentially devastating consequences. Compelling and abundant evidence demonstrates that some non-native species can become invasive and produce major ecosystem disruptions and even native species extinction. Unfortunately, we still cannot be certain which non-native species will be the most detrimental (e.g., Combining native and non-native species together into a single biodiversity index would not only inflate biodiversity estimates and risk promoting the spread of invasive non-native species but would also

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.308
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0170.032

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 head0.029
GPT teacher head0.251
Teacher spread0.222 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations68
Published2018
Admission routes1
Has abstractyes

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