Bibliographic record
Abstract
The Historical Interim Temporal Shipping (HITS) database contains ship densities on a seasonal or monthly basis for five classes of vessels: super tanker, large tanker, tanker, merchant, and fishing. A new version of the database was recently created and included new fishing vessel densities for selected areas derived from recent United Nations Food and Agriculture Organization (FAO) reports. As part of a noise model validation effort, more detail about the fishing vessels in the area off Nova Scotia was required, and this report presents the results of that study as well as documents the process required to derive fishing boat densities. The study determined how many boats of different sizes are fishing in a given location off Nova Scotia in different seasons. The study largely consisted of obtaining, analyzing, distilling, and quantifying information on individual species and the boats that catch them. Nearly all the information used to determine the seasonal distribution of fishing boats off Nova Scotia was obtained from the Canadian Department of Fisheries and Oceans (DFO). The study found nearly 5000 boats fishing in the region, with nearly 94% less than 45 ft long, and provides the geographic distribution of fishing vessels for 15 species of fish.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".