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Record W6950939681 · doi:10.5683/sp2/fr6h9s

Morphologic and metabolic data of Philaenus spumarius froghoppers

2021· dataset· en· W6950939681 on OpenAlexaff

Bibliographic record

VenueBorealis · 2021
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsXylemPeduncle (anatomy)SarcomereTurgor pressureTermitidae

Abstract

fetched live from OpenAlex

AbstractThe xylem sap of vascular plants is an unlikely source of nutrition, being both nutrient poor and held under tensions (negative pressures) that can exceed 1 MPa. But some insects feed on xylem sap exclusively, extracting copious quantities using a muscular cibarial pump. However, neither the strength of the insect’s suction, nor the direct energetic cost of xylem ingestion, have ever been quantified. Philaenus spumarius froghoppers were used to address these gaps in our knowledge. Micro-CT scans of its cibarium and measurements of cibarial muscle sarcomere length revealed that P. spumarius can generate a maximum tension of 1.3 ± 0.2 MPa within its cibarium. The energetic cost of xylem extraction was quantified using respirometry to measure the metabolic rate (MR) of P. spumarius while they fed on hydroponically-grown legumes, while xylem sap excretion rate and cibarial pumping frequency were simultaneously recorded. Increasing the plants’ xylem tensions up to 1.1 MPa by exposing their roots to polyethylene glycol did not reduce the insects’ rate of xylem excretion, but significantly increased both MR and pumping frequency. We conclude that P. spumarius can gain energy feeding on xylem sap containing previously reported energy densities and at xylem tensions up to their maximum suction capacity.

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)0.000
metaresearch head score (Gemma)0.001
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.004

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 head0.041
GPT teacher head0.258
Teacher spread0.217 · 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
GenreDataset

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
Published2021
Admission routes1
Has abstractyes

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