Venter Institute’s Global Ocean Sampling expedition [1–3]. These
Bibliographic record
Abstract
papers describe the initial analyses of several gigabasepairs ’ worth of sequence data from oceanic microbes collected during the Sorcerer II expedition, as the ship made her way down from Canada, through the Panama Canal, and finally out beyond the Galapagos Islands well into the tropical Pacific and the South Pacific Gyre. Results from the first foray of this research mission into the Sargasso Sea were published three years ago [4]. As described in the accompanying Synopsis [5], the new voyage has added information from multiple biomes and several-fold more data. Analysis of these data poses not only scientific challenges [6], but also significant legal hurdles. Craig Venter is no stranger to issues of intellectual property—his previous incarnation as the president of Celera saw him embroiled in controversy over the decision to “privatize ” aspects of his company’s work in sequencing the human genome. Now, at the head of the Global Ocean Sampling project, Venter finds himself on the side of greater accessibility, negotiating the claims of individual governments on the genomic wealth within their waters. In particular, as of this writing, there is an active negotiation with the Ecuadorian government (which has seen more than one change of power since the expedition began) over restricting commercial reuse of these data. Henry Nicholls describes this tangled legal landscape in an accompanying Feature [7]. Although extensive in scope, the papers presented here only touch the surface of the wealth of information to be gleaned from these data, which are freely available for all to explore from their desktops: the trace reads and processed data have been deposited in the National Center for
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".