PERD Iceberg Sighting Database for the Grand Banks of Canada (compilation report)
Bibliographic record
Abstract
This report is a compilation of the individual reports that have been prepared to describe the database and annual updates to the PERD Grand Banks Iceberg Sighting Database(formerly known as the PERD Iceberg Population Database). The database was initiated in 1998 to assimilate all of the information on the annual iceberg population on the East Coast. It is updated each year with new data from the previous ice season. Also, improvements to the database are usually made in identifying new sources of data and eliminating duplicate records. The individual annual update reports are presented here beginning with the most recent. The work is supported as part of the Ice-Structure Interaction Activity of the Program of Energy Research and Development (PERD). For information on this database, contact Denise.Sudom@nrc-cnrc.gc.ca (contact name changed 2015-03; formerly Garry Timco).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.030 |
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".