Experimental Assessment of the Magnitude and Sources of Lake Sturgeon Egg Mortality
Bibliographic record
Abstract
Abstract Mortality during early life stages can greatly affect annual recruitment. Despite the importance to population abundance and community composition, quantitative estimates of the sources and magnitude of early life mortality in natural environments are generally lacking for many fish species. We conducted a field experiment to quantify egg mortality during incubation for Lake Sturgeon Acipenser fulvescens . Fertilized Lake Sturgeon eggs were placed in replicated exclosures in the Black River, Michigan, at a known spawning location. Incubation conditions were modified using four exclosure treatments differing in mesh size that simulated different levels of access by predators and water flow regimes (0.1–0.6 m/s). Egg mortality through 80% of the incubation period was high (average 91%) and varied significantly (75–97%) across treatments. Treatments with reduced predator access and low water velocity experienced the highest levels of cumulative egg mortality. Developmental arrest was a larger source of mortality (84%) than the combined effect of predation and scour or de‐adhesion (16%). We also documented a significant treatment by time (day of incubation) interaction, indicating that although cumulative rates of mortality may not vary significantly among spawning sites, the relative contributions of different sources of mortality can vary greatly at different times during egg incubation.
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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) | 0.001 | 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 | 0.001 |
| 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".