Extrinsic Factors Influencing Somatic Growth of Lake Sturgeon
Bibliographic record
Abstract
Abstract Variation in somatic growth of Lake SturgeonAcipenser fulvescenshas primarily been attributed to temperature (latitude), but recent studies have suggested additional complexities. Based on populations in Manitoba, Canada, a multivariate analysis was conducted to reevaluate the extrinsic factors influencing growth of Lake Sturgeon. Length at ages 3, 6, 9, and 12 and the following six explanatory variables were examined: latitude, mean annual air temperature,pH, conductivity, juvenile Lake Sturgeon density (gill‐netCPUE), and water velocity. Length‐at‐age variables were highly correlated, but considerable variation for fish of a given age was observed along the flow axes of individual rivers (and even within multibasin reservoirs). For example, fork length at age 12 ranged from 520 to 906 mm. After dropping latitude due to colinearity with both temperature andpH, a redundancy analysis based on the remaining five explanatory variables explained 79.9% of the variation in the four length‐at‐age variables. Lake Sturgeon growth was negatively correlated with bothCPUE(41.2% of variation) and velocity (19.2%) and positively correlated with conductivity (14.8%). The temperature andpHvariables lacked statistical significance for inclusion in a forward‐selection model, regardless of which other variables were included. Our results suggest that the influence of temperature (latitude) on Lake Sturgeon growth may previously have been overstated because habitat variation was not accounted for. Indeed, because populations exploit a diversity of river types across the species range, the discussion of growth needs to be placed in the context of habitat. Individuals from two Lake Sturgeon populations occupying similar latitudes can exhibit markedly different rates of somatic growth, with much of the variation being explained by juvenile density, water velocity, and conductivity. As Lake Sturgeon recovery proceeds, fisheries managers will need to contend with dynamic growth trajectory responses to increasing abundance, much as they would for other 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 | 0.000 | 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".