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Record W2608973293 · doi:10.1371/journal.pbio.2001829

The School Malaise Trap Program: Coupling educational outreach with scientific discovery

2017· article· en· W2608973293 on OpenAlexafffundabout
Dirk Steinke, Vanessa Breton, Emily A. Berzitis, Paul D. N. Hebert

Bibliographic record

VenuePLoS Biology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversity of Guelph
FundersTD Friends of the Environment FoundationGenome Canada
KeywordsBarcodeOutreachBiologyBiodiversityStewardship (theology)Diversity (politics)Identification (biology)FaunaGenomicsEvolutionary biologyEcologyGenomeGeneticsAnthropology

Abstract

fetched live from OpenAlex

The School Malaise Trap Program (SMTP) provides a technologically sophisticated and scientifically relevant educational experience that exposes students to the diversity of life, enhancing their understanding of biodiversity while promoting environmental stewardship. Since 2013, the SMTP has allowed 15,000 students at 350 primary and secondary schools to explore insect diversity in Canadian schoolyards. Students at each school collected hundreds of insects for an analysis of DNA sequence variation that enabled their rapid identification to a species. Through this hands-on approach, they participated in a learning exercise that conveys a real sense of scientific discovery. As well, the students contributed valuable data to the largest biodiversity genomics initiative ever undertaken: the International Barcode of Life project. To date, the SMTP has sequenced over 80,000 insect specimens, which includes representatives of 7,990 different species, nearly a tenth of the Canadian fauna. Both surprisingly and importantly, the collections generated the first DNA barcode records for 1,288 Canadian species.

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)0.009
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.334

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0080.003
Scholarly communication0.0040.003
Open science0.0030.014
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0160.003

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 head0.024
GPT teacher head0.258
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 designNot applicable
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

Citations40
Published2017
Admission routes3
Has abstractyes

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