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Record W4380862952 · doi:10.5281/zenodo.7430683

IPBES Invasive Alien Species Assessment: Full report

2023· report· en· W4380862952 on OpenAlexaff
Helen E. Roy, Aníbal Pauchard, Peter Stoett, Tanara Renard Truong

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typereport
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAlienAlien speciesInvasive speciesEcologyGeographyBiologyFisheryMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Thematic Assessment Report on Invasive Alien Species and their Control of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) is composed of 1) a Summary for Policymakers (SPM), will be approved by the IPBES Plenary at its 10th session in Sep 2023 in Bonn, Germany (IPBES-10); and 2) a set of six Chapters, will be accepted by the IPBES Plenary. All products of the assessment are available on Zenodo. Find all the links on https://www.ipbes.net/invasive-alien-species-assessment and on the side bar "has part".

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)0.006
metaresearch head score (Gemma)0.004
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0320.028

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 head0.077
GPT teacher head0.296
Teacher spread0.218 · 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
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

Citations9
Published2023
Admission routes1
Has abstractyes

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