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Record W4388017563 · doi:10.3390/hydrobiology2040036

Insights into Diatom Substrate Preferences in the Inter-Tidal Zone of a Subarctic Coast

2023· article· en· W4388017563 on OpenAlexafffundabout
Emilie Arseneault, Reinhard Pienitz, Julie Carrière, Émilie Saulnier‐Talbot

Bibliographic record

VenueHydrobiology · 2023
Typearticle
Languageen
FieldMaterials Science
TopicDiatoms and Algae Research
Canadian institutionsCenter for Northern StudiesUniversité Laval
FundersInstitut Nordique De Recherche En Environnement Et En Santé Au TravailUniversité Laval
KeywordsDiatomSubstrate (aquarium)Subarctic climateBayHabitatEcologyOceanographyBenthic zoneEnvironmental scienceBioindicatorSalinityNutrientSeabirdEstuaryGeographyGeologyBiology

Abstract

fetched live from OpenAlex

Diatoms are reliable environmental bioindicators, but their application in coastal environments remains limited. Substrate has been put forward in the literature as an important variable in determining diatom habitat preferences. This study focuses on benthic diatom assemblages and their relationship with substrate specificity in a subarctic tidal environment, which could be relevant for environmental monitoring and management. A variety of substrates were sampled and physicochemical variables measured in various areas of the Bay of Sept-Îles region (northern Gulf of Saint-Lawrence, Canada). We recorded 606 species at 14 sites from 11 substrate types to determine the associations between diatoms and their habitats. Our results suggest that the variability of assemblages in the bay is the result of a combination of the identified variables (temperature, salinity, and total dissolved solids), explaining 26.5% of the variation, and other unmeasured variables (e.g., nutrients, wave action, and currents). Substrate was not identified as a significant variable in the statistical analyses. However, some common species in the surveyed assemblages appeared to show preferences for the substrates they colonized.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.030
GPT teacher head0.291
Teacher spread0.261 · 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 designBench or experimental
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

Citations3
Published2023
Admission routes3
Has abstractyes

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