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Tabula Microcebus

2021· dataset· en· W4394238005 on OpenAlexaboutno aff
Camille Ezran, Angela Oliveira Pisco, The Tabula Microcebus Consortium

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldComputer Science
TopicCellular Automata and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental science

Abstract

fetched live from OpenAlex

Our goal is to make sequence data rapidly and broadly available to the scientific community as a community resource. It is our intention to publish the work of this project in a timely fashion, and we welcome collaborative interaction on the project and analyses. However, considerable investment was made in generating these data and we ask that you respect rights of first publication and acknowledgment as outlined in the Toronto agreement (Toronto International Data Release Workshop Authors. Prepublication data sharing. Nature. 2009 Sep 10;461(7261):168-70). By accessing these data, you agree not to publish any articles containing analyses of genes, cell types or transcriptomic data on a whole atlas or tissue scale prior to initial publication by the Tabula Microcebus Consortium and its collaborating scientists. If you wish to make use of restricted data for publication or are interested in collaborating on the analyses of these data, please use the contact form available from our data portal (https://tabula-microcebus.ds.czbiohub.org/). Redistribution of these data should include the full text of the data use policy.<br>

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.091
Threshold uncertainty score0.978

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1140.023

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 head0.024
GPT teacher head0.250
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

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
Published2021
Admission routes1
Has abstractyes

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