Performance of Instream Jordan–Scotty Salmon Egg Incubators Under Different Installation and Sedimentation Conditions
Bibliographic record
Abstract
Abstract Fish stocking, in which natural populations are supplemented with progeny through some unnatural means, is widespread and takes many forms. Long-term benefits of population viability are generally greatest when individuals are subjected to as little artificial rearing as possible. For salmonids, instream incubation enables embryo exposure to natural conditions during a critical developmental period. The Jordan–Scotty incubator is a new tool that facilitates the ability to conduct instream incubation on a semi-large scale. However, the performance of this type of incubator is affected by sedimentation. We quantified sediment accumulation within Jordan–Scotty incubators when installed using four potential methods in a blocked design that eliminated confounding space–time variables. Although sediment accumulation was highly variable, there appeared to be no benefit in taking the extra effort to install the incubators under gravel as recommended by the manufacturer. Incubator exposure to streamflow can be achieved in several ways and reduces sedimentation. We recommend the use of pre-installed plastic milk crates as incubator receptacles.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".