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Supplementary material to "Predictive mapping of organic carbon stocks and accumulation rates in surficial sediments of the Canadian continental margin"

2023· preprint· en· W4387668458 on OpenAlexaboutno aff
Graham Epstein, Susanna Fuller, Dipti Hingmire, Paul G. Myers, M. Angélica Peña, Clark Pennelly, Julia K. Baum

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMargin (machine learning)Continental marginTotal organic carbonGeologyCarbon fibersOceanographyEnvironmental scienceGeochemistryPaleontologyEnvironmental chemistryChemistryComputer science

Abstract

fetched live from OpenAlex

The following plots show the final set of predictor variables available for predictive mapping of seafloor sediments across Canada.All plots are shown with the x-axis indicating the Longitude and y-axis the Latitude within the co-ordinate reference system EPSG:3573 WGS 84 -North Pole Lambert Azimuthal Equal Area Canada.The final resolution of all raster layers is 200 m x 200 m; however, they cannot be visually reproduced at this resolution at their full extent, therefore the display of the data inherently includes some aggregation.Plots are predominantly shown with a continuous colour scale from low values in grey and high values in green.Bioregion is shown as 12 discrete categories (see Table 2 of main text); BPI (benthic position index), VRM (vector ruggedness measure) and curvature measures are shown in quantile groups to better visualise spatial contrasts in the data.For some of the plots shown in a continuous colour scale, the delineation between lower values may not be visually apparent due to highly right skewed data.For further information on the methods and data sources see Table 1 and Sections 2.2 and 2.3 of the main text.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.546
Threshold uncertainty score0.847

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.5460.102

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 head0.253
Teacher spread0.229 · 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.

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
Published2023
Admission routes1
Has abstractyes

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