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Record W2734737348 · doi:10.1177/0023677217718004

A simple and inexpensive way to document simple husbandry in animal care facilities using QR code scanning

2017· article· en· W2734737348 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLaboratory Animals · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Supply Chain Traceability
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAnimal husbandrySimple (philosophy)Computer scienceBusinessProcess (computing)Animal healthHealth recordsRecord keepingHealth careDatabaseMedicineEcologyVeterinary medicineBiologyAgriculturePolitical scienceOperating system

Abstract

fetched live from OpenAlex

Record keeping within research animal care facilities is a key part of the guidelines set forth by national regulatory bodies and mandated by federal laws. Research facilities must maintain records of animal health issues, procedures and usage. Facilities are also required to maintain records regarding regular husbandry such as general animal checks, feeding and watering. The level of record keeping has the potential to generate excessive amounts of paper which must be retained in a fashion as to be accessible. In addition it is preferable not to retain within administrative areas any paper records which may have been in contact with animal rooms. Here, we present a flexible, simple and inexpensive process for the generation and storage of electronic animal husbandry records using smartphone technology over a WiFi or cellular network.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.717

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 studies0.0010.000
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 head0.032
GPT teacher head0.287
Teacher spread0.255 · 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