NuSEDS - New Salmon Escapement Database System
Bibliographic record
Abstract
Salmon escapement data constitute important corporate knowledge which must be adequately maintained and accessible. The Salmon Escapement Database (NuSEDS) is the DFO Pacific Region’s central database that stores individual spawner survey data records, spawner abundance estimates and the linkages between the two. Annual abundance estimates are maintained by population, as defined by freshwater location and run timing. Each population is referenced to the location of the stream mouth. The watershed-coding system provides unique stream identification and incorporates the natural organization, direction, and hierarchical nature of stream channels and their tributaries. The NuSEDS database currently reports salmon spawning observations for 9100+ individual populations but escapement estimates (all levels of survey intensity) are available for 9800+ populations. This database contains historic population data starting in the 1920’s (older data for some rivers exists in other formats). Prior to 1995 a standardized form (BC-16) was used to summarize the estimate of the spawning population size, but the historical database lacked the capacity to describe the number of observations, individual counts or methods used to estimate the abundance of the population. In 1995, responsibility for salmon enumeration was moved to the DFO Science section. At that time, the database was re-written to include descriptive information for each abundance estimate, providing underlying data and the estimation method(s). Many of the historic estimates prior to 1995 are labeled Unspecified Returns because the database was limited to storing one estimate for any given stock. As time and resources permit these data are being replaced with more accurate categorization. With the introduction of the Wild Salmon Policy (2005), individual populations within NuSEDS can now be grouped by Conservation Unit.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.091 | 0.127 |
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".