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Record W2756048268 · doi:10.1139/as-2016-0020

Periphytic diatom community structure in thermokarst ecosystems of Nunavik (Québec, Canada)

2017· article· en· W2756048268 on OpenAlexaffvenueabout
Frédéric Bouchard, Valentin Proult, Reinhard Pienitz, Dermot Antoniades, Roxane Tremblay, Warwick F. Vincent

Bibliographic record

VenueArctic Science · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalCenter for Northern StudiesInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsThermokarstDiatomEnvironmental scienceEcologyLake ecosystemEcosystemLittoral zoneVegetation (pathology)PermafrostBiology

Abstract

fetched live from OpenAlex

Climate change is causing rapid permafrost degradation across Arctic and subarctic regions, resulting in changes in the size, abundance, and structure of thermokarst (thaw) ponds and lakes. The main objectives of this study were to analyze periphytic diatom communities and their affinity to vegetation substrates in thermokarst ecosystems located in the eastern Hudson Bay region and to establish a first inventory of diatom assemblages and the associated littoral vegetation in these systems. Some generalist diatom species, including Tabellaria flocculosa, occupied all ecological niches in the water bodies. In contrast, genera such as Eunotia and Pinnularia were more specialized and generally concentrated on moss substrates. Shoreline vegetation and thermokarst pond/lake littoral morphology (slope) resulted in limnological conditions that differed between sites and ultimately affected diatom community structure. Our results show that both shoreline vegetation and diatom communities are diverse in thermokarst ecosystems, and their species composition depends mostly on site-specific properties (available microhabitats, local pond/lake morphology) rather than limnological conditions that are closely aligned with regional ecoclimatic conditions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.999

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.243
Teacher spread0.212 · 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

Citations6
Published2017
Admission routes3
Has abstractyes

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