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Record W6944423959 · doi:10.17632/ymt427t3dm.1

Simultaneous Nitrogen Aggregation and Cooling (SNAC) Model

2025· dataset· en· W6944423959 on OpenAlexaboutno aff

Bibliographic record

VenueExplore Bristol Research · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsNitrogenWater coolingThermalCore (optical fiber)Simple (philosophy)

Abstract

fetched live from OpenAlex

The Simultaneous Nitrogen Aggregation and Cooling (SNAC) model can be applied to zoned diamonds to calculate cooling rates and initial temperatures, such that modelled nitrogen aggregation states match those that were measured with FTIR. It requires input of the nitrogen concentrations and corresponding aggregation states of the core and rim of a single diamond, the respective ages of each zone, and the kimberlite eruption age. We provide a simple version of the model, in which all input parameters (including the cooling rate and initial temperature) can be adjusted manually, and an automated model, which refines the thermal parameters iteratively. Please consult our paper (Wincott et al., forthcoming) for further detail of how the model works and an example of its application to diamonds from Diavik, Canada.

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)0.003
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.086
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.003

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 head0.121
GPT teacher head0.409
Teacher spread0.288 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2025
Admission routes1
Has abstractyes

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