MétaCan
Menu
Back to cohort
Record W4220999590 · doi:10.1242/jeb.242358

A perspective on insect water balance

2022· article· en· W4220999590 on OpenAlexafffund
Michael J. O’Donnell

Bibliographic record

VenueJournal of Experimental Biology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInsectWater balanceSurface waterHindgutDesiccationWater transportWater vaporTranspirationEcologyBiologyEnvironmental scienceEnvironmental chemistryChemistryWater flowBotanySoil scienceLarvaEnvironmental engineeringGeology

Abstract

fetched live from OpenAlex

Insects have a large ratio of surface area to volume because of their small size; thus, they face the potential for desiccation in the terrestrial environment. Nonetheless, they constitute over half of identified species and their success on land can be attributed, in part, to adaptations that limit water loss and allow for effective gains of water from food, fluids or atmospheric water vapour. Reduction of water loss from the gut involves sophisticated mechanisms of ion recycling and water recovery by epithelia of the Malpighian tubules and hindgut. Water loss across the body surface is greatly reduced by the evolution of very thin but highly impermeable lipid-rich layers in the epicuticle. Respiratory water loss can be reduced through effective spiracular control mechanisms and by mechanisms for convective rather than diffusive gas exchange. In addition to extracting water from food sources, some insects are capable of absorption of atmospheric water vapour through processes that have evolved independently in multiple groups.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.005
Open science0.0020.001
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0120.004

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 head0.021
GPT teacher head0.271
Teacher spread0.250 · 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 designTheoretical or conceptual
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

Citations42
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Experimental BiologySame topicPhysiological and biochemical adaptationsFrench-language works237,207