Timing and Extent of Drift of Shortnose Sturgeon Larvae in the Saint John River, New Brunswick, Canada
Bibliographic record
Abstract
Abstract Little is known about the dispersal of Shortnose Sturgeon Acipenser brevirostrum larvae in the wild. In the Saint John River, New Brunswick, we captured a total of 2,251, 460, 2,100, and 2,083 larvae in 2008–2011, respectively; abundance estimates ranged between 21,000 (2009) and 244,687 larvae (2008). A substantial reduction in larval numbers (49–76%) was recorded over the 4.5‐km distance between the two sampling transects deployed in 2008–2010. We found no consistent pattern of larval distribution across the channel, but we recorded a consistent, significant preference for nighttime (dusk to dawn) over daytime dispersal. Generalized linear models were used to examine the timing and extent of larval migration in the Saint John River during the study period. Logistic models incorporating water temperature and Mactaquac Dam discharge provided good predictions of the timing of larval migration. The probability of larval presence was highest when water temperature reached 15°C. At this temperature, larvae were predicted to disperse when nighttime total dam discharge was 20 106 to 30 106 m3. The extent of larval migration was described using negative binomial models, which indicated that dam discharge and transect location significantly influenced the number of drifting larvae. However, data variability was high, reducing predictive capability. Our findings include the first report of Shortnose Sturgeon larval abundances in the Saint John River. The predictions of timing and extent of drift provide information for future sampling and conservation efforts during this vulnerable period as well as insight into the relationships between environmental variables and larval drift in this protected species.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".