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Record W1966377410 · doi:10.1371/journal.pone.0123947

Lab-on-a-Bird: Biophysical Monitoring of Flying Birds

2015· article· en· W1966377410 on OpenAlexaff
Abdurrahman Gümüş, Seoho Lee, Syed Saad Ahsan, Kolbeinn Karlsson, Richard M. Gabrielson, Christopher G. Guglielmo, David W. Winkler, David Erickson

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsWestern University
FundersDivision of Civil, Mechanical and Manufacturing InnovationNational Science Foundation
KeywordsUric acidBiosensorBiologyPotentiostatEcologyBiochemistryChemistryElectrode

Abstract

fetched live from OpenAlex

The metabolism of birds is finely tuned to their activities and environments, and thus research on avian systems can play an important role in understanding organismal responses to environmental changes. At present, however, the physiological monitoring of bird metabolism is limited by the inability to take real-time measurements of key metabolites during flight. In this study, we present an implantable biosensor system that can be used for continuous monitoring of uric acid levels of birds during various activities including flight. The system consists of a needle-type enzymatic biosensor for the amperometric detection of uric acid in interstitial fluids. A lightweight two-electrode potentiostat system drives the biosensor, reads the corresponding output current and wirelessly transfers the data or records to flash memory. We show how the device can be used to monitor, in real time, the effects of short-term flight and rest cycles on the uric acid levels of pigeons. In addition, we demonstrate that our device has the ability to measure uric acid level increase in homing pigeons while they fly freely. Successful application of the sensor in migratory birds could open up a new way of studying birds in flight which would lead to a better understanding of the ecology and biology of avian movements.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.228

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 communication0.0000.000
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 head0.209
GPT teacher head0.292
Teacher spread0.083 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations14
Published2015
Admission routes1
Has abstractyes

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