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Record W2043716520 · doi:10.3897/zookeys.22.222

Biodiversity and biosystematic research in a brave new 21st century information-technology world

2009· article· en· W2043716520 on OpenAlexaff
Robert S. Anderson, Christopher Majka

Bibliographic record

VenueZooKeys · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsNova Scotia HospitalCanadian Museum of Nature
Fundersnot available
KeywordsBiodiversityVariety (cybernetics)DocumentationEnvironmental resource managementBiologyData scienceEcologyComputer scienceEconomics

Abstract

fetched live from OpenAlex

A variety of challenges to biodiversity and biosystematics research are discussed. Despite escalating estimates of the biodiversity of the planet, resources being devoted to advance this knowledge have been in decline. Despite the proliferation of information technologies, the focus of knowledge has frequently shifted to making information readily available, rather than generating new information. The principles of authorial responsibility and of explicit documentation of knowledge are under siege. The shortfall of investment in training, research, and collections management (the ''taxonomic deficit'') has lead to a ''taxonomic impediment'' to ecological research, at a time when rates of extinction appear to be rising dramatically. The contents of present volume represent stepping-stones of biodiversity research – a discipline vital to the future of life on the planet.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.007
Science and technology studies0.0040.013
Scholarly communication0.0130.016
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.040
GPT teacher head0.274
Teacher spread0.234 · 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 designTheoretical or conceptual
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

Citations5
Published2009
Admission routes1
Has abstractyes

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