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Record W2152416085 · doi:10.1644/10-mamm-a-051.1

Quantitative magnetic resonance: a rapid, noninvasive body composition analysis technique for live and salvaged bats

2010· article· en· W2152416085 on OpenAlexfundno aff
Liam P. McGuire, Christopher G. Guglielmo

Bibliographic record

VenueJournal of Mammalogy · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsnot available
FundersMinistry of Natural Resources
KeywordsMagnetic resonance imagingComposition (language)ZoologyBiologyNuclear magnetic resonanceMedicinePhysicsRadiologyArt

Abstract

fetched live from OpenAlex

Abstract Quantitative magnetic resonance (QMR) is a new technology for measuring body composition of live, nonanesthetized animals (fat mass, lean mass, and total body water) in 4 min or less. We conducted a validation study to compare QMR body composition analysis of 3 species of bats (mass range 5.77–31.30 g) with traditional chemical extraction. In addition to scans of live animals, we tested the effectiveness of QMR for salvaged specimens (bats killed by wind turbines) and the effects of carcass temperature. Our analysis indicates that QMR body composition analysis is effective for live and salvaged animals. Frozen carcasses could not be analyzed, but results were not dramatically affected for specimens at 4°C and 37°C. QMR analysis eliminates the need to euthanize animals to determine body composition precisely, allows rapid and efficient data collection, and makes it possible to follow individuals longitudinally through time.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.250
Teacher spread0.234 · 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 designObservational
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

Citations51
Published2010
Admission routes1
Has abstractyes

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