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Record W2265290824 · doi:10.2166/wqrj.2010.045

The Eastern Canadian Diatom Index (IDEC) Version 2.0: Including Meaningful Ecological Classes and an Expanded Coverage Area that Encompasses Additional Geological Characteristics

2010· article· en· W2265290824 on OpenAlexafffundabout
Isabelle Lavoie, Martine Grenier, Stéphane Campeau, Peter Dillon

Bibliographic record

VenueWater Quality Research Journal · 2010
Typearticle
Languageen
FieldMaterials Science
TopicDiatoms and Algae Research
Canadian institutionsTrent UniversityUniversité du Québec à Trois-RivièresInstitut National de la Recherche Scientifique
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsDiatomNova scotiaSTREAMSIndex (typography)GeographyEcologyInterpretation (philosophy)BiologyArchaeologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract In 2006, the Eastern Canadian Diatom Index (IDEC: Indice Diatomées de l'Est du Canada) was developed to monitor the biological integrity of streams and rivers in Eastern Canada. The first version of the IDEC has been successfully used to evaluate the biological status of numerous sites. A new version of the index (IDEC 2.0) was recently developed to cover a larger geographic area that encompasses additional geological characteristics. IDEC 2.0 is now applicable for the biological assessment of streams in Quebec, Ontario, New Brunswick, Nova Scotia, and Prince Edward Island. Moreover, the approach used to define the biological interpretation of the index values was revised. IDEC 2.0 presents ecologically meaningful differences among biological communities (ecological thresholds or class boundaries) based on diatom biotypes, providing a more relevant interpretation of the diatom community changes along the alteration gradient.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.008
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.154
GPT teacher head0.397
Teacher spread0.243 · 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 designObservational
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

Citations13
Published2010
Admission routes3
Has abstractyes

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