Calcified structures: the use of metal signatures to understand life history and estimate age in Lake Sturgeon (Acipenser fulvescens)
Bibliographic record
Abstract
Understanding population structure in fisheries management is dependent on knowledge of a variety of factors, such as age structure and age at maturation of individuals in that population. Lake Sturgeon are a large freshwater fish found throughout North America’s inland lakes and rivers. Although Lake Sturgeon populations were once numerous, anthropogenic pressure has led to the majority of them being designated as endangered, threatened, or of special concern. Due to their current status, efforts to conserve and restore populations are extensive. In this thesis, I investigated two distinct themes regarding the age structure of Lake Sturgeon, using the chemical composition of their pectoral fin rays. The first experimental chapter focused on testing a relatively new aging approach that uses the annular periodicity in fin ray microchemistry to assist with traditional aging. For this chapter, I compared age precision and accuracy measurements across three age assignment methods (two chemical methods and the traditional visual method) to determine if this new approach could provide true age estimates for known-age fish. Results indicated that both accuracy and precision measurements were higher when using the elemental concentrations to assist with traditional aging ultimately supporting the method as a supplementary aging technique. Throughout the second experimental chapter, I examined the morphological and chemical composition of pectoral fin rays across life stages in a closed population of Lake Sturgeon to determine if physiological influences related to onset of sexual maturity (OSM) could be identified. Results indicated that levels of Ba, Pb, Mn, Mg, and Zn increased after OSM while growth zone width decreased. Using this information, a random forest model was established to discriminate year-specific signatures to before or after OSM. The model could reliably discriminate signatures to before or after OSM with high success (98.8%), suggesting that elemental signatures and growth zone width may prove useful in determining sexual maturity status in Lake Sturgeon. Using the microchemistry of calcified structures to age fish or assess life history status related to reproduction may have the potential to significantly advance current fisheries management strategies that primarily rely on the chemistry of structures to assess environmental impacts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".