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
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.022 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".