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Record W4399437290 · doi:10.1038/s41597-024-03396-9

Three-dimensional reconstruction of high latitude bamboo coral via X-ray microfocus Computed Tomography

2024· article· en· W4399437290 on OpenAlexafffundabout
Thomas J. Williams, Philip J. Basford, Orestis L. Katsamenis, Martin Solan, Gavin L. Foster, Christopher D. Standish, Jasmin A. Godbold, Philippe Archambault

Bibliographic record

VenueScientific Data · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBamboo properties and applications
Canadian institutionsUniversité LavalArcticNet
FundersFisheries and Oceans CanadaMemorial University of NewfoundlandResearch Councils UKUniversité Laval
KeywordsBambooCoralComputed tomographyTomographyLatitudeGeologyRadiologyBiologyGeodesyMedicineOceanographyBotany

Abstract

fetched live from OpenAlex

The skeletons of long-lived bamboo coral (Family Keratoisididae) are promising archives for deep-water palaeoceanographic reconstructions as they can record environmental variation at sub-decadal resolution in locations where in-situ measurements lack temporal coverage. Yet, detailed three dimensional (3D) characterisations of bamboo coral skeletal architecture are not routinely available and non-destructive investigations into microscale variations in calcification are rare. Here, we provide high-resolution micro-focus computed tomography (µCT) data of skeletal density for two species of bamboo coral (Acanella arbuscula: 5 specimens, voxel size, 15 µm (central branch scans) and 50 µm (complete structure scan); Keratoisis sp.: 4 specimens, voxel size, 15 µm) collected from the Labrador Sea and Baffin Bay deep-water basins, Eastern Canadian Arctic. These data provide reference models useful for developing methods to assess structural integrity and other fine-scale complexities in many biological, geological, and industrial systems. This will be of wider value to those investigating structural composition, arrangement and/or composition of complex architecture within the fields and subdisciplines of biology, ecology, medicine, environmental geology, and structural engineering.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.227
Teacher spread0.191 · 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 designNot applicable
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

Citations4
Published2024
Admission routes3
Has abstractyes

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