Bibliographic record
Abstract
The geographical distribution and production of the Barents Sea capelin (Mallotus villosus, Osmeridae) is modelled by the use of a state-variable optimization technique (dynamic programming), where the main objective of individuals always is to maximize fitness, or total expected reproduction (RO), by selecting the most profitable habitats through time. Fitness is gained by successful reproduction (a function of size) during the spawning season on the breeding grounds off northern Norway. The environment (predators, temperature and zooplankton prey) is determined by a meteorologically forced circulation model for the year 1980, creating a spatial and seasonal fluctuation in the environment. Predation from cod is the main source of mortality, and the distribution of the cod (Gadus morhua) stock is assumed to vary with temperature. Growth is predicted from a bioenergetic model, incorporating the cost of swimming between feeding areas and spawning grounds. Field data of the capelin stock recorded during autumn cruises from 1979 is implemented at the start of the model, and then this stock is modelled through 1980 and the first months of 1981. Model predictions are compared with the observed distribution of capelin in autumn 1980. Habitat selection has consequences for the dynamics of the population and growth of individuals, demonstrating the importance of combining external (environmental) and internal (evolutionary) forcing to understand and predict the dynamics of fish populations. This study is the first application of dynamic programming to model the dynamics and ecology of horizontal fish migration, and we suggest that the method may be developed into a useful tool for the management of short-lived species.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.990 | 0.993 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".