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Record W6899334305 · doi:10.5886/ujwtvvs2

University of British Columbia Herbarium (UBC) - Algae Collection from University of British Columbia

2013· other· en· W6899334305 on OpenAlexaboutno aff

Bibliographic record

VenueCanadensys · 2013
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsHerbariumAlgaeFish <Actinopterygii>Research councilFisheries ResearchOn boardFoundation (evidence)

Abstract

fetched live from OpenAlex

This dataset includes all digitized algae in the UBC collection, encompassing approximately 80% of the collection. During the course of Robert Scagel’s academic career (1952-1986) and assisted by graduate students and postdoctoral fellows carrying out research on benthic marine algae, Scagel oversaw the expansion of the Phycological Herbarium to its present size of over 67,000 specimens. During this period, with support from U.B.C., the National Research Council of Canada (NSERC), the Defence Research Board of Canada, the Fisheries Research Board of Canada, the Provincial Fisheries Department, the B.C. Research Council and the National Science Foundation (U.S.), collecting activities were carried out all along the Pacific Coast from central California to Alaska, including the Aleutian Islands as far west as Attu Island. Collections from these surveys were deposited in the Phycological Collection at U.B.C. The Alaska representation in the Phycological Collection has been significantly augmented since 1986 by donations from Sandra Lindstrom.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.397
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0670.001

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 head0.005
GPT teacher head0.147
Teacher spread0.142 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2013
Admission routes1
Has abstractyes

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