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Record W3048331172 · doi:10.1139/as-2019-0024

Are different benthic communities in Arctic delta lakes distinguishable along a hydrological connectivity gradient using a rapid bioassessment approach?

2020· article· en· W3048331172 on OpenAlexaffvenueabout
Ryan W. Scott, Suzanne E. Tank, Xiaowa Wang, Roberto Quinlan

Bibliographic record

VenueArctic Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversity of AlbertaEnvironment and Climate Change CanadaYork University
Fundersnot available
KeywordsBenthic zoneDeltaEnvironmental scienceInvertebrateEcologyArcticRiver deltaOrdinationCommunity structureBenthosHydrology (agriculture)Physical geographyOceanographyGeographyGeologyBiology

Abstract

fetched live from OpenAlex

Aquatic habitats in the Canadian Arctic are expected to come under increasing stress due to projected effects of climate change. There is a need for community-based biomonitoring programs to observe and understand the effects of these stressors on the environment. Here we present results from a 5 year annual sampling program of benthic invertebrates from lakes in the Mackenzie Delta, Northwest Territories, using a rapid bioassessment protocol. Connectivity between the deltaic lakes and main channels is a major driver of lake function and is expected to be substantially impacted by climate change. Lakes were selected along a gradient of connectivity based on sill elevation above the river. Using multivariate analyses of community structure, we determined that benthic assemblages responded to differences in connection time among lakes. This response was detected using a coarse taxonomic level that could be applied by community groups or volunteers but was stronger when invertebrates were identified to the family and genus levels. A secondary gradient was observed that corresponded to productivity gradients in lakes that are isolated from the river during summer. We show that benthic assemblages have potential use as sensitive indicators of climate-mediated changes to the hydrology of lakes in the Mackenzie Delta.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.072
GPT teacher head0.251
Teacher spread0.179 · 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.

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

Citations5
Published2020
Admission routes3
Has abstractyes

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