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Record W2774888862 · doi:10.14430/arctic4679

Updates to the Marine Algal Flora of the Boulder Patch in the Beaufort Sea off Northern Alaska as Revealed by DNA Barcoding + Supplementary Appendix 1 (See Article Tools)

2017· article· en· W2774888862 on OpenAlexafffundvenue
Trevor T. Bringloe, Kenneth H. Dunton, Gary W. Saunders

Bibliographic record

VenueARCTIC · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaNew Brunswick Innovation Foundation
KeywordsDNA barcodingKelp forestArcticBiodiversityFlora (microbiology)EcologyEnvironmental DNABiologyMarine ecosystemGeographyAlgaeEcosystemFisheryPaleontology

Abstract

fetched live from OpenAlex

Since its discovery four decades ago, the Boulder Patch kelp bed community in the Beaufort Sea has been an important site for long-term ecological studies in northern Arctic Alaska. Given the difficulties associated with identifying species of marine algae on the basis of morphology, we sought to DNA barcode a portion of the flora from the area and update a recently published species list. Genetic data were generated for 20 species in the area. Fifty-five percent of the barcoded flora confirmed the morphological species identifications. Five barcoded species revealed what are likely misapplied names to the Boulder Patch flora; the updated names include Ahnfeltia borealis, Phycodrys fimbriata, Pylaiella washingtoniensis, Rhodomela lycopodioides f. flagellaris, and Ulva prolifera. The remaining four species require taxonomic work and possibly represent new records for the Boulder Patch. Our observations indicate that we need considerably more research to understand marine macroalgal biodiversity in the Arctic. Supplementing Arctic species lists using genetic data will be essential in establishing an accurate and reliable baseline for monitoring changes in ecosystem biodiversity driven by long-term changes in regional climate.

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 score0.320
Threshold uncertainty score0.997

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.225
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

Citations10
Published2017
Admission routes3
Has abstractyes

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Same venueARCTICSame topicMarine and coastal plant biologyFrench-language works237,207