Evaluating the effects of controlled flows on historical spawning site access, reproduction and recruitment of lake sturgeon <i>Acipenser fulvescens</i>
Bibliographic record
Abstract
Abstract Lake sturgeon Acipenser fulvescens spawn at the base of Kakabeka Falls, a 39 m waterfall on the Kaministiquia River, a tributary to Lake Superior. Access to this historical spawning site can be restricted or delayed due to hydroelectric flow fluctuations that coincide with the A. fulvescens spawning season. The objectives of this study were to determine (a) the necessary flow conditions that facilitate spawning site access; (b) quantity and duration of flow required for successful spawning and dispersal of larvae; and (c) evaluate recruitment of juvenile A. fulvescens in relation to flow. A. fulvescens spawning migrations were tracked using a stationary telemetry receiver that logged the movements of 166 A. fulvescens fitted with radio‐transmitters. Unrestricted access to the spawning site was facilitated when spawning flow was controlled at 23 m3 s−1 in 2004 and 17 m3 s−1 in 2006. Fluctuating (0.5–8.5 m3 s−1) and delayed spawning flows resulted in restricted and delayed access to the spawning site. Flow duration for successful egg incubation, hatch and larval dispersal was determined by sampling larvae using drift nets and quantified using cumulative temperature units (CTU). Over 10 years, 10,083 larvae were captured between 31 May and 20 July with 97% of the drift occurring prior to 30 June. From the date of first spawning to the end of larval dispersal took an average of 38.6 days, and the mean CTU value was 398.6. In general, a minimum flow of approximately 14.5 m3 s−1 from the date of initial spawning to the accumulation of c. 400 CTU ensured successful hatch and larval dispersal. During the timeframe of this study, recruitment was variable. This study described the complex and variable reproductive life history of A. fulvescens and defined spawning flow requirements ecologically, which can be used to develop operational provisions at hydropower facilities to ensure successful reproduction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".