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Record W1999418935 · doi:10.1071/wr05024

Comparison of the healing process in hot and cold brands applied to harbour seal pups (Phoca vitulina)

2006· article· en· W1999418935 on OpenAlexaff
Pierre‐Yves Daoust, G M Fowler, Wayne T. Stobo

Bibliographic record

VenueWildlife Research · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsBedford Institute of OceanographyUniversity of Prince Edward Island
Fundersnot available
KeywordsPhocaCold warSeal (emblem)BiologyFisheryGeography

Abstract

fetched live from OpenAlex

Hot branding has been used for many years by researchers to identify seals from a long distance. In livestock, cold branding has been proposed as an alternative because it is thought to be less painful. The purpose of this study was to compare the healing process of hot and cold brands applied to harbour seal pups (Phoca vitulina). A total of 306 animals was branded with a unique set of four characters: three applied for 3–5 s with an iron heated to 500°C, and one applied for either 10 or 20 s with an iron frozen to –175°C. At three subsequent times over 10 weeks, 43, 41 and 51 animals, respectively, were recaptured, the macroscopic appearance of their brands recorded, and biopsies taken for microscopic examination. Cold brands had a faster healing rate than hot brands. However, they resulted in less destruction of hair follicles, and cold brands applied for 20 s caused more depigmentation. Regrowth of hair follicles could subsequently obscure brands, while depigmentation reduces the contrast between the brand and the surrounding fur. Cold brands applied for 20 s also caused more extensive deep vascular damage, which subsequently may have resulted in deeper wounds in some of these brands. Yet, macroscopically, other cold brands, or portions thereof, were almost invisible. On the basis of this short-term study, the technique of cold branding that we used in harbour seals does not appear to be as reliable as that of hot branding to provide permanent legible brands.

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)0.002
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 score0.412
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.118
GPT teacher head0.389
Teacher spread0.271 · 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

Citations12
Published2006
Admission routes1
Has abstractyes

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