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Record W3087324726 · doi:10.1093/ae/tmaa035

The Value of Local Farms for Insect Conservation: Local Teaching Opportunities

2020· article· en· W3087324726 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Entomologist · 2020
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsTrent University
Fundersnot available
KeywordsInsectValue (mathematics)AgroforestryBusinessGeographyEcologyBiologyMathematicsStatistics

Abstract

fetched live from OpenAlex

EVEN FARMS THAT SEEM TO LACK SUCH SPECIALIZED REGIONS PROVIDE UNIQUE HABITATS FOR INSECTS AND OTHER WILDLIFE. Farmland creates unique conservation challenges, including opportunities to increase biodiversity (Schieltz and Rubenstein 2016). Studies of local and small-scale farm systems are urgently needed, or the opportunity to understand the biodiversity of these areas will be lost. Increasingly, producers are under pressure to remove fencerows and hedges, install drainage tiles, and bring more land into production. Our study provides an example of the importance of studying small, overlooked farm habitats using simple, readily accessible methods and student help. Entomological field work is often taught using examples from our experiences working in exotic locations. Although these experiences can be fascinating, we wondered if such stories might convey a false impression that conservation is only important for wildlife in distant, fragile landscapes. Conservation is also very important at the local level. The goal of our project was to act according to this conviction, and to show that new or exciting data such as new distribution records could be found at a local farm close to the city.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.735

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.133
GPT teacher head0.351
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