MétaCan
Menu
Back to cohort
Record W2914773960

An Update on the Invertebrate Zoology Collection at the University of California, Santa Barbara 

2018· article· en· W2914773960 on OpenAlexaboutno aff
Rachel Behm, Pamela Horsley, Katja C. Seltmann

Bibliographic record

VenueeScholarship (California Digital Library) · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsOutreachInvertebrateEndangered speciesBiodiversityEntomologyGeographyLibrary scienceEcologyArchaeologyBiologyHabitatComputer sciencePolitical science
DOInot available

Abstract

fetched live from OpenAlex

We are in the second year of a project to revitalize entomology at the University of California, Santa Barbara and have new updates of its impact on the campus to report. The UC Santa Barbara Natural History Collection (UCSB) at the Cheadle Center for Biodiversity and Ecological Restoration formed the UCSB Invertebrate Zoology collection from about 10K historical specimens that were found in a basement on the UCSB campus. The collection has continued to grow rapidly through Coastal California arthropod survey efforts, donated student collections, and faculty research projects. New results from the project include the formation of an outreach program through the UCSB Extension Department and discovery of several extirpated rare or endangered insects that once occurred on the UCSB campus. We will report on how the discovery of extirpated species on campus has received some press and the development of a collection on campus has raised the profile of entomology and insect conservation in the area.This poster was presented at the 2018 Entomological Collections Network meeting held in Vancouver, Canada on November 10-11.

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)0.005
metaresearch head score (Gemma)0.008
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: Other
Teacher disagreement score0.158
Threshold uncertainty score0.530

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.009
Science and technology studies0.0030.001
Scholarly communication0.0060.005
Open science0.0030.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.1580.115

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 head0.016
GPT teacher head0.200
Teacher spread0.184 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueeScholarship (California Digital Library)Same topicSpecies Distribution and Climate ChangeFrench-language works237,207