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Record W2945833430 · doi:10.1594/pangaea.898677

Stable silicon isotope composition of deep sea sponges and co-located seawater silicon isotopic compositions collected from the North Atlantic

2019· dataset· en· W2945833430 on OpenAlexaboutno aff
Katharine Hendry, Lucie Cassarino, Stephanie L Bates, Timothy Culwick, Molly Frost, Claire Goodwin, Kerry L. Howell

Bibliographic record

VenueFigshare · 2019
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsnot available
FundersHorizon 2020Seventh Framework Programme
KeywordsSeawaterSiliconOceanographyComposition (language)Stable isotope ratioIsotopes of siliconIsotopeDeep seaGeologyEnvironmental chemistryEnvironmental scienceChemistryArt

Abstract

fetched live from OpenAlex

This data release contains the stable silicon isotope composition of deep sea sponges collected from the North Atlantic, and co-located seawater silicon isotopic compositions. Three sites were surveyed: the Labrador Sea, Nova Scotia and Porcupine Bight. The samples were collected as part of the European Research Council project ICY-LAB (ERC-2015-STG grant agreement number 678371), EU Horizon 2020 project SponGES (H2020-BG-2015-2 grant agreement number 679849), and EU Seventh Framework Programme EUROFLEETS2 (FP7/2007-2013 grant agreement number 312762).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.018

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 head0.021
GPT teacher head0.226
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2019
Admission routes1
Has abstractyes

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