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Record W3133828925 · doi:10.1139/as-2020-0060

Stream diatom assemblages in an Arctic catchment: diversity and relationship to ecosystem-scale primary production

2021· article· en· W3133828925 on OpenAlexvenueno aff
Breena Riley, David A. Seekell

Bibliographic record

VenueArctic Science · 2021
Typearticle
Languageen
FieldMaterials Science
TopicDiatoms and Algae Research
Canadian institutionsnot available
FundersKnut och Alice Wallenbergs StiftelseUmeå UniversitetNational Science Foundation
KeywordsDiatomBeta diversityArcticEcosystemTundraEcologyBenthic zoneAlpha diversityDrainage basinSTREAMSGeographyEnvironmental scienceBiodiversityPhysical geographyBiologyCartography

Abstract

fetched live from OpenAlex

We quantified benthic diatom diversity in streams in the Miellajokka catchment, about 200 km north of the Arctic circle in Sweden. Beta diversity among sites was related to local-scale environmental heterogeneity (occurring on the order of 1 km or less), and its magnitude was equal (Sørensen Index = 0.62) to levels previously reported for rivers on regional environmental gradients across hundreds of kilometres of Arctic Fennoscandia. Species turnover was the dominant (77%) component of beta diversity in the Miellajokka catchment. Small, stress-tolerant taxa dominated the assemblages, and there were no clear patterns of functional class among sites. Site ordinates from non-metric dimensional scaling were most strongly correlated with flood frequency (r = 0.83) and water temperature (r = 0.89), which was higher in harsh tundra sites than below treeline. Additionally, site ordinates were correlated (r = 0.83) with ecosystem-scale gross primary production — indicative of a link between diatom diversity and ecosystem function. Our results advance understanding of patterns diatom diversity in Arctic streams by quantifying local-scale variation that is understudied in this region, and by identifying the consequences of this local-scale diversity for an ecosystem-scale process.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.077
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.292
Teacher spread0.260 · 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 teacher head, 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

Citations4
Published2021
Admission routes1
Has abstractyes

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