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Record W2241103556 · doi:10.1017/cbo9780511763175

The Diatoms

2010· book· en· W2241103556 on OpenAlexaff
John P. Smol, Matthew L. Julius, H. J. B. Birks, R. Jan Stevenson, Euan D. Reavie, Richard W. Battarbee, Roland I. Hall, Helen Bennion, Julie A. Wolin, S. C. Fritz, Anson W. Mackay, André F. Lotter, Marianne S. V. Douglas, Sarah A. Spaulding, Pauline Snoeijs, Rosa Trobajo, Sherri R. Cooper, Christopher S. Lobban, Benjamin P. Horton, Oscar E Romero, Amy Leventer, Richard W. Jordan

Bibliographic record

VenueCambridge University Press eBooks · 2010
Typebook
Languageen
FieldMaterials Science
TopicDiatoms and Algae Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsGlossaryDiatomSection (typography)PaleolimnologyEutrophicationEcologyOceanographyEnvironmental scienceData scienceComputer scienceGeologyBiology

Abstract

fetched live from OpenAlex

This much revised and expanded edition provides a valuable and detailed summary of the many uses of diatoms in a wide range of applications in the environmental and earth sciences. Particular emphasis is placed on the use of diatoms in analysing ecological problems related to climate change, acidification, eutrophication, and other pollution issues. The chapters are divided into sections for easy reference, with separate sections covering indicators in different aquatic environments. A final section explores diatom use in other fields of study such as forensics, oil and gas exploration, nanotechnology, and archaeology. Sixteen new chapters have been added since the first edition, including introductory chapters on diatom biology and the numerical approaches used by diatomists. The extensive glossary has also been expanded and now includes over 1,000 detailed entries, which will help non-specialists to use the book effectively.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.059
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0590.039

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 head0.015
GPT teacher head0.219
Teacher spread0.204 · 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
GenreEmpirical

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

Citations406
Published2010
Admission routes1
Has abstractyes

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Same venueCambridge University Press eBooksSame topicDiatoms and Algae ResearchFrench-language works237,207