Variation in Fork-to-Total Length Relationships of North American Lake Trout Populations
Bibliographic record
Abstract
Abstract Length of fish species with forked tails, such as the Lake Trout Salvelinus namaycush, can be measured as total (TL), fork (FL), or standard (SL) length, although individual studies of such species often rely on only one measurement, which hinders comparisons among studies. To determine if variation in the relationship between FL and TL among Lake Trout populations affected estimates of FL from TL, we compared length relationships within Lake Trout populations sampled in multiple years, among multiple locations within lakes, among lakes, and from all samples from across the species' range. Samples were from across the geographic range of the species and a wide range of lake sizes (1.31–82,100 km2) to represent the full range of variation in abiotic and biotic variables expected to influence the FL:TL relationship. The functional relationship for estimating FL (mm) from TL (mm) was FL = 0.91 × TL − 8.28 and TL from FL was TL = 1.09 × FL + 9.05. Error induced by length conversion was less when using a length relationship from a different year in the same lake than from a different area in the same lake or from a different lake. Estimation error was lowest when using an overall length conversion from across the species' range, which suggests the overall relationship could be used whenever a more accurate length conversion is not available for a population of interest. Our findings should be useful for providing a standardized model for converting FL to TL (and TL to FL) for Lake Trout, such as comparing published findings of different measurement units, converting measurement units by agencies or institutions that change sampling methods over time, or programs that use different sampling methods among areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".