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Record W2549949221 · doi:10.1111/phor.12167

The Practical Application Of 3D Vision in the Field: Measuring Reindeer (<i>Rangifer Tarandus</i>) Antler Growth Velocities

2016· article· en· W2549949221 on OpenAlex
Derek D. Lichti, Jeremy Steward, Jacky Chow, John R. Matyas

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Photogrammetric Record · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsAntlerArtificial intelligenceObject (grammar)Range (aeronautics)Feature (linguistics)Field (mathematics)Computer scienceRangingComputer visionGeographyMathematicsEngineeringArchaeology

Abstract

fetched live from OpenAlex

Abstract Advances in three‐dimensional (3D) optical imaging have made possible precise and accurate measurements of many scenes, ranging from engineering to architecture to art. However, measurements of some 3D objects are more difficult to obtain than others, particularly if the edges do not feature regular geometry, the colour is dark and variable, and if the object moves haphazardly. Such objects occur regularly in biology, and the present study illustrates some of the challenges of evaluating such objects. The growing antlers of three live reindeer ( Rangifer tarandus ) is presented as an example of how 3D imaging, specifically time‐of‐flight range imaging, can be used to solve to a reasonable extent a problem that is very difficult to approximate using traditional techniques. Mean antler growth velocities of the order of 7 to 9 mm/day were estimated, using the proposed methodology, from data of these three animals collected over a seven‐week period.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.017
GPT teacher head0.283
Teacher spread0.266 · 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