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Record W4392656415 · doi:10.3389/frym.2024.1331857

How Will Climate Change Affect Pikas’ Favorite Snacks?

2024· article· en· W4392656415 on OpenAlex
Emily Monk, Karli Weatherill, Chris Ray, Ashley L. Whipple, Johanna Varner

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

VenueFrontiers for Young Minds · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsMemorial University of Newfoundland
FundersColorado Mesa UniversityNational Science Foundation
KeywordsAffect (linguistics)Climate changeGeographyPsychologyEcologyBiologyCommunication

Abstract

fetched live from OpenAlex

Many animals are herbivores, which means they get all their nutrients from eating plants. American pikas are cute rabbit relatives that eat plants in the mountains. But alpine winters are harsh, so pikas spend their entire summer gathering and storing plants to eat under the winter snow. Just like people, pikas in Colorado have a favorite food: a plant called alpine avens. This plant species is a special pika snack because it contains natural preservatives called phenolics, which keep the food fresh all winter. We studied how climate change is affecting this important feature of the pika’s favorite meal. Alpine avens contains more phenolics now than it did 30 years ago, so they preserve better in storage. But there is a catch: these preservatives can be hard to digest. Studies like this help us start to understand the many complicated ways that climate change affects herbivores like pikas.

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 score0.750
Threshold uncertainty score0.410

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 communication0.0000.001
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 head0.021
GPT teacher head0.225
Teacher spread0.204 · 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