MétaCan
Menu
Back to cohort
Record W2260981190 · doi:10.1594/pangaea.847703

Western Alaska Lake Database

2015· dataset· en· W2260981190 on OpenAlexaboutno aff
Prajna R Lindgren, Guido Grosse, V. E. Romanovsky

Bibliographic record

VenueFigshare · 2015
Typedataset
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDatabaseArchaeologyGeographyGeologyOceanographyComputer science

Abstract

fetched live from OpenAlex

This vector data layer covers 6 major lake districts (Baldwin Peninsula, Kobuk Delta, Selawik Lowland, Northern Seward Peninsula, Central Seward Peninsula, and Yukon-Kuskokwim Delta) in the northern and central sub-regions of the Western Alaska Landscape Conservation Cooperative (WALCC) region and consists of polygons of lakes with areas equal or greater than 1.0 ha. More than 58000 Lakes were mapped from Landsat TM and ETM+ imagery acquired between 1972 and 1975, 2002 and 2009, and 2013 and 2014 using Object-Based Image Analysis (OBIA) techniques with an classification accuracy of 96%. The spatial image resolution of Landsat TM and ETM+ is 30 m. Permafrost characteristics and surficial geology associated with lake polygons were determined from the Alaska permafrost map (Jorgenson et al. 2008).

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 score0.434
Threshold uncertainty score0.822

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 science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.6200.187

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 head0.133
GPT teacher head0.284
Teacher spread0.150 · 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueFigshareSame topicWater Quality and Resources StudiesFrench-language works237,207