Bibliographic record
Abstract
The Fisheries and Oceans Canada, Pacific Region Institute of Ocean Science (IOS) Zooplankton Database is a MS Access Application for data entry, storage, and retrieval of zooplankton and related data. This OBIS resource contains a small subset of species distribution records extracted from the IOS database. The 'Line P' dataset contains records of plankton collected at Ocean Station Papa (P26 data) in the Pacific Ocean between 1965 to 1980. Data from Ocean Station P, 50°N and 145°W, were collected by various researchers and analyzed to many different levels of taxonomic resolution. Data from 1956 to 1965 was analyzed as biomass, wet weight per cubic metre only. Portion of samples from 1964 to 1967 were analyzed for five species groups and samples from 1968 to 1980 had species identification plus stages, counts and lengths for select taxonomic groups and/or species with the rest of the sample being classified under “remainder” (and assigned the scientific name Animalia). Majority of the early samples were taken by NORPAC conical net with 351micron white Nitex mesh, mouth diameter 0.42m. Later it was switched to a 0.5m diameter SCOR net with 351 micron mesh. In 1966 and 1967 they played around with both black and white nets before going with black from 1968 onwards. Please read the introduction of the technical report (Canadian Technical Report of Fisheries and Aquatic Sciences 2056, 1995) to get an idea of the scope of the data and its limitations. Of great importance is the section on nets and net inter-calibration plus precautions and uncertainties. Project name change was done in 1995 when the Line P Monitoring Program was initiated, Station P to Line P.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.065 |
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".