Multicompartment Gravel Bed Flume to Evaluate Swim-Up Success of Salmonid Larvae
Bibliographic record
Abstract
Abstract Swim-up success, the proportion of fry emerging from a gravel redd, is difficult to quantify in the field, and currently available laboratory systems are limited. We used custom-built gravel bed flumes to assess the swim-up success of Rainbow Trout Oncorhynchus mykiss, Brook Trout Salvelinus fontinalis, and Cutthroat Trout Oncorhynchus clarkii. Flumes were built with compartments to separate individual egg batches, eggs were buried under gravel, and oxygenated water was supplied to simulate upwelling through the gravel. Temperature, dissolved oxygen, pH, and flow were constant between compartments and flumes operating in parallel. Swim-up success was scored both by the emergence of fry relative to the number of eggs placed in the flume (59–85% depending on species) and the hatching success (proportion of eggs that hatch and resorb their yolk sac) of a sample from the same clutch reared in a vertical incubator (62–84% depending on species). The gravel bed flumes could be used to estimate the swim-up success of salmonid egg clutches from hatchery stocks or fish from the wild or under experimental regimes of relevance to fishery or environmental assessment, including changes in pH, siltation, temperature, toxicants, or events simulating floods or droughts. Received July 12, 2012; accepted October 11, 2012
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".