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Record W4391839466 · doi:10.1371/journal.pbio.3002497

Harnessing online digital data in biodiversity monitoring

2024· article· en· W4391839466 on OpenAlexaboutno aff
Andrea Soriano‐Redondo, Ricardo A. Correia, Vijay Barve, Thomas M. Brooks, Stuart H. M. Butchart, Ivan Jarić, Ritwik Kulkarni, Richard J. Ladle, Ana Sofía Vaz, Enrico Di Minin

Bibliographic record

VenuePLoS Biology · 2024
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsnot available
FundersH2020 Marie Skłodowska-Curie ActionsH2020 European Research CouncilTekniikan AkatemiaFundação para a Ciência e a TecnologiaKoneen SäätiöGrantová Agentura České RepublikyCzech Centre for Phenogenomics, Institute of Molecular Genetics of the Czech Academy of SciencesEuropean Commission
KeywordsBiodiversityBiologyCitizen scienceComplement (music)Digital dataEnvironmental resource managementGlobal biodiversityData scienceEcologyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Online digital data from media platforms have the potential to complement biodiversity monitoring efforts. We propose a strategy for integrating these data into current biodiversity datasets in light of the Kunming-Montreal Global Biodiversity Framework.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.998

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.176
GPT teacher head0.377
Teacher spread0.201 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations18
Published2024
Admission routes1
Has abstractyes

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