Assessment of spatio-temporal variation in larval abundance of lake sturgeon (<i>Acipenser fulvescens</i>) in the Rupert River (Quebec, Canada), using drift nets
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Bibliographic record
Abstract
The Rupert River is one of the largest tributaries on the east coast of James Bay. Lake sturgeon (Acipenser fulvescens) is present all along the main stem where several spawning grounds have been located, four of which are major spawning grounds that have been studied at km 216, 281, 290 and 362. The total number of drifting larvae was estimated with drift nets set along transverse transects at km 212, 276, and km 287 from 2007 to 2009, and at km 361 in 2008 and 2009, using a new technique, namely, a Doppler current meter to measure water velocity within transect sub-sections corresponding to Voronoï polygons. There was a substantial, persistent difference in the number of larvae produced by the four main spawning areas. On average, the most productive site (km 276) produced over five times more larvae than the least productive site (km 361). Average estimated numbers were 41,194 at km 212, 176,840 at km 276, 106,212 at km 287, and 30,642 at km 361. Temporal variations were of much less amplitude than spatial differences. Between 2007 and 2009, interannual variations were not significant, except at km 212, despite differences in river flow during incubation and larval drift. The number of gravid females and the quality of spawning grounds would likely be the main factors influencing the total number of larvae. Vertical distribution of larvae is variable between sites and years, and shows a slight tendency for larvae to be more surface oriented. Higher flow near the surface would partly explain larger surface drifting of larvae. Transverse distribution is uneven and often associated with the location of the spawning grounds and the river flow. Given the uneven vertical and transverse distribution of larvae, an effective sampling strategy should cover the complete water column and full river width. Where depth exceeds 3 m, at least two stacked nets are recommended. In large rivers, filtering close to 1% of total river flow should result in acceptable confidence intervals, allowing a good comparison of the number of larvae in space and time.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it