Dissolved inorganic carbon (DIC), total alkalinity, water temperature, salinity, dissolved oxygen, nutrients and other hydrographic and chemical data collected from discrete samples and profile observations during the CCGS Louis S. St-Laurent Joint Ocean Ice Study (JOIS-10) cruise (EXPOCODE 18SN20100915) in the Arctic Ocean from 2010-09-15 to 2010-10-15 (NCEI Accession 0232263)
Bibliographic record
Abstract
This dataset includes discrete profile measurements of Dissolved inorganic carbon (DIC), total alkalinity, water temperature, salinity, dissolved oxygen, nutrients and other hydrographic and chemical data collected from discrete samples and profile observations during the CCGS Louis S. St-Laurent Joint Ocean Ice Study (JOIS-10) cruise (EXPOCODE 18SN20100915) in the Arctic Ocean from 2010-09-15 to 2010-10-15. The Joint Ocean Ice Study (JOIS) in 2010 involved the collaboration of Fisheries and Oceans Canada researchers with colleagues primarily from the U.S.A and Japan. This program forms an important Canadian contribution to international climate research programs and is comprised of two ongoing programs: the Beaufort Gyre Exploration Project (BGEP), a collaboration with Woods Hole Oceanographic Institution scientists and the Pan-Arctic Climate Investigation (PACI), a collaboration with Japan Agency for Marine-Earth Science and Technology (JAMSTEC) scientists. In 2010 JOIS also included ancillary programs carried out by researchers from: the International Arctic Research Center (IARC) in Fairbanks Alaska; Tokyo University of Marine Science and Technology (TUMSAT), Japan; Kitami Institute of Technology (KIT), Japan.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.007 | 0.006 |
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".