Mysis relicta and kokanee salmon (Oncorhynchus nerka) in Okanagan Lake, British Columbia : from 1970 and into the future
Bibliographic record
Abstract
The opossum shrimp (Mysis relicta) was introduced into Okanagan Lake, British Columbia (BC), in 1966 in order to serve as an intermediate food item for kokanee salmon (Oncorhychus nerka). However, beginning in the early 1970s, kokanee began a sharp decline in abundance. In the search for reasons for the kokanee decline, two factors were identified: mysid competition with kokanee over zooplankton resources and reduced nutrient loads to the lake. Between 1970 and 2000, the M. relicta population increased 20-fold and nutrients in the lake fell to one quarter of 1970 levels. Using the Ecopath with Ecosim (EwE) software, an Ecopath model of Okanagan Lake in 1970 was built. This model attempted to account for all biomass within Okanagan Lake and contained 16 groups and 2 fisheries. This base 1970 model was then run through the Ecosim module and biomass was predicted for all groups from 1970 to 2020. For the 1970 to 2000 period, mysid and kokanee biomass were tracked by the program with high accuracy. Two key findings for this period were that Mysis relicta appears to have been responsible for the original kokanee decline, but reduced nutrient loads to Okanagan Lake are currently keeping the kokanee at depressed levels. From 2000-2020, Ecosim was used in a forecasting mode and solutions were examined which may help rehabilitating the kokanee population. The current mysid fishery does not have the capacity (30t year-1) to catch the number of mysids required (300t year-1) to aid kokanee populations to any great degree. Nutrient additions appear to be able to boost kokanee abundance in the lake without increasing mysid populations greatly. However, a combined approach involving nutrient additions with an intensified mysid fishery could allow kokanee abundance to approach 1970 levels.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".