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Record W4403120414 · doi:10.25080/nvyf1037

Ecological and Spatial Influences on the Genetics of Cumacea (Crustacea: Peracarida) in the Northern North Atlantic

2024· article· en· W4403120414 on OpenAlexaff
Justin Gagnon, Nadia Tahiri

Bibliographic record

VenueProceedings of the Python in Science Conferences · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsEcologyEcological geneticsGeographyEvolutionary biologyBiologyDemographySociology

Abstract

fetched live from OpenAlex

The peracarid taxon Cumacea is an essential indicator of benthic quality in marine ecosystems. This study investigated the influence of environmental (i.e., biological or ecosystemic), climatic (i.e., meteorological or atmospheric), and spatial (i.e., geographic or regional) variables on their genetic variability and adaptability in the Northern North Atlantic, focusing on Icelandic waters. We analyzed partial sequences of the 16S rRNA mitochondrial gene from 62 Cumacea specimens. Using the aPhyloGeo software, we compared these sequences with relevant variables such as latitude (decimal degree) at the end of sampling, wind speed (m/s) at the start of sampling, O 2 concentration (mg/L), and depth (m) at the start of sampling. Our analyses revealed variability in spatial and biological variables, reflecting the diversity of ecological requirements and benthic habitats. The most common Cumacea families, Diastylidae and Leuconidae, suggest adaptations to various marine environments. Phylogeographic analysis showed a divergence between specific genetic sequences and two habitat variables: wind speed (m/s) at the start of sampling and O 2 concentration (mg/L). This observation may indicate the possibility of varying local adaptations in response to these fluctuating conditions. These results reinforce the importance of further research into the relationship between Cumacea genetics and global environmental variables to interpret the evolutionary dynamics and adaptation of these deep-sea organisms. This study sheds much-needed light on the acclimatization of invertebrates to climate change, anthropogenic pressures, and marine habitat management, potentially contributing to the evolution of more effective conservation strategies and policies to protect these vulnerable ecosystems. The aPhyloGeo Python package is freely and publicly available on GitHub and PyPi , providing an invaluable tool for future research.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.034
GPT teacher head0.270
Teacher spread0.236 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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