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Record W6907411750 · doi:10.21966/ymv6-8024

Bongo Zooplankton Data from the R/V TINRO, NOAA Bell M. Shimada, F/V Northwest Explorer and R/V CCGS Sir John Franklin during the 2022 International Year of the Salmon Pan-Pacific Winter High Seas Expedition

2022· dataset· en· W6907411750 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHakai Institute · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsFisheries and Oceans CanadaUniversity of British Columbia
Fundersnot available
KeywordsZooplanktonAbundance (ecology)International watersPacific ocean

Abstract

fetched live from OpenAlex

This data set contains the zooplankton data collected using paired bongo tows from the R/V TINRO, NOAA Bell M. Shimada, F/V Northwest Explorer and R/V CCGS Sir John Franklin from February 5 - April 17, 2022 in the North Pacific Ocean. The paired bongo nets (60 cm in diameter, 253 micron mesh size) were deployed to a depth of approximately 250 m and retrieved vertically at 1 m s-1. Collected data are used to offer insights in zooplankton community composition, density and distribution. After the bongo net deployment and recovery, the net was rinsed down into the cod end. Volume of sea water filtered was determined using flowmeters. This dataset includes samples that were preserved in formalin. These samples were enumerated and identified to the lowest taxonomic rank possible or practical. Abundance (individuals per cubic meter) was recorded by species, lifestage, sex and size (range).

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.

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 categoriesMeta-epidemiology (narrow), Open science, 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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0060.006
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.026
GPT teacher head0.247
Teacher spread0.222 · 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

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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