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Record W6907713865 · doi:10.21966/r603-b338

Northwest Calvert sea wrack temporal data, Central Coast, British Columbia (2016-2017)

2016· dataset· en· W6907713865 on OpenAlexaffabout

Bibliographic record

VenueHakai Institute · 2016
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTransectSeagrassBiomass (ecology)ProductivityShoreTemperate climate

Abstract

fetched live from OpenAlex

This dataset contains the collection site locations, visit dates, and weights of each species of shore cast algae or seagrass found during each site visit. Dead, shore-cast seaweeds and seagrasses (commonly called sea wrack) provide an important vector of marine-derived nutrients to low productivity terrestrial environments, such as beaches. However, little is known about the processes that facilitate wrack transport, deposition, and accumulation in coastal temperate British Columbia. Throughout the course of one year, I visited three sites on a bi-monthly basis to document temporal changes in wrack biomass and species composition. At each site, wrack was measured along 12 permanent 1-meter wide belt-transects. The transects ran perpendicular to the water's edge, originating at the forest-beach interface and extending to the ocean at low tide. I found wrack biomass to be similar throughout all four seasons, wrack species composition, however, varied. My results suggest sea wrack is a consistent vector of potential nutrients from the marine to the terrestrial environment in British Columbia. Detailed methods and conclusions in the MSc thesis found in the linked folder. Sara Wickham – University of Victoria, Brian Starzomski – University of Victoria; John Reynolds – Simon Fraser University; Chris Darimont – University of Victoria

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0070.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.042

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 head0.271
Teacher spread0.237 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2016
Admission routes2
Has abstractyes

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