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Record W4283697989 · doi:10.3389/fmars.2022.898852

Description and Spatial Modelling of Benthic Communities Distribution in the Canadian Arctic Archipelago

2022· article· en· W4283697989 on OpenAlexafffundabout
Philippe-Olivier Dumais, Cindy Grant, Bodil A. Bluhm, Laure de Montety, Lisa Tréau de Coeli, Jean‐Éric Tremblay, Philippe Archambault

Bibliographic record

VenueFrontiers in Marine Science · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsUniversité LavalArcticNet
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationArcticNetCanada First Research Excellence FundW. Garfield Weston Foundation
KeywordsBenthic zoneArcticOceanographyArchipelagoGeographyEcologyEnvironmental scienceSea iceFisheryGeologyBiology

Abstract

fetched live from OpenAlex

In the Arctic, sea ice loss has already transformed the dominant sources and periodicity of primary production in some areas, raising concerns over climate change impacts on benthic communities. Considered to be excellent indicators of environmental changes, benthic invertebrates play important roles in nutrient cycling, sediment oxygenation and decomposition. However, this biological component of the Canadian Arctic Archipelago (CAA) is still somewhat poorly studied compared to other Arctic regions. To partly fill this need, this study aims to evaluate benthic community composition and its relationship to significant environmental drivers and to develop spatial predictive explanatory models of these communities to expand coverage between sampled stations across the Kitikmeot Sea region and Parry Channel. Results from previously collected samples suggest that biodiversity is higher in this region compared to the Beaufort and Baffin Seas, two adjacent regions to the West and East, respectively. This finding leads to the main hypothesis that (1) benthic communities are succeeding one another, forming an ecotone (transition area) between the Beaufort Sea and the Baffin Sea. Other hypotheses are that (2) Pacific Ocean water influence through the CAA can explain part of this gradient, and that (3) terrigenous inputs affect the distribution of species. Overall, results tend to confirm hypotheses. Generalized Linear Models (GLMs) (with R2 up to 0.80) clearly displayed a succession in community distribution from Queen-Maud Gulf (Southwest) to Lancaster Sound (Northeast). Such models can be useful in identifying potential biodiversity hotspots and as a baseline for marine spatial planning purposes. Further, Pacific origin water (traced with concentrations of nitrate relative to phosphate) and terrigenous inputs (traced with silicate concentrations) were related to species and community distribution. Given that these two inputs/factors are generally increasing in the Canadian Arctic, their influence on benthic communities may also be seen to increase in the upcoming years.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.217
Teacher spread0.186 · 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 designSimulation or modeling
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
Published2022
Admission routes3
Has abstractyes

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