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Record W2014557018 · doi:10.1080/10256016.2015.969256

Hydrogen isotope measurement of bird feather keratin, one laboratory's response to evolving methodologies

2014· article· en· W2014557018 on OpenAlexfundno aff
Majie Fan, David L. Dettman

Bibliographic record

VenueIsotopes in Environmental and Health Studies · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
FundersTanzania Commission for Science and TechnologyMemorial University of Newfoundland
KeywordsFeatherIsotopeHydrogen isotopeKeratinEnvironmental scienceRadiochemistryEnvironmental chemistryChemistryBiologyEcologyPhysicsNuclear physicsPaleontology

Abstract

fetched live from OpenAlex

Hydrogen in organic tissue resides in a complex mixture of molecular contexts. Some hydrogen, called non-exchangeable (H(non)), is strongly bound, and its isotopic ratio is fixed when the tissue is synthesized. Other pools of hydrogen, called exchangeable hydrogen (H(ex)), constantly exchange with ambient water vapor. The measurement of the δ(2)H(non) in organic tissues such as hair or feather therefore requires an analytical process that accounts for exchangeable hydrogen. In this study, swan feather and sheep wool keratin were used to test the effects of sample drying and capsule closure on the measurement of δ(2)H(non) values, and the rate of back-reaction with ambient water vapor. Homogenous feather or wool keratins were also calibrated at room temperature for use as control standards to correct for the effects of exchangeable hydrogen on feathers. Total δ(2)H values of both feather and wool samples showed large changes throughout the first ∼6 h of drying. Desiccant plus low vacuum seems to be more effective than room temperature vacuum pumping for drying samples. The degree of capsule closure affects exchangeable hydrogen equilibration and drying, with closed capsules responding more slowly. Using one control keratin standard to correct for the δ(2)H(ex) value for a batch of samples leads to internally consistent δ(2)H(non) values for other calibrated keratins run as unknowns. When placed in the context of other recent improvements in the measurement of keratin δ(2)H(non) values, we make recommendations for sample handing, data calibration and the reporting of results.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.069
GPT teacher head0.329
Teacher spread0.260 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

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