Data and script - Quantifying migratory capacity and dispersal of the invasive Tench (Tinca tinca) in the St. Lawrence River using otolith chemistry
Bibliographic record
Abstract
Description:Datafiles and R scripts from Morissette et al. 2021 article published in Canadian Journal of Fisheries and Aquatic Sciences Study abstract:The study of distribution and dispersal of invasive fishes is challenging during the early stages of invasion. Quantification of trace elements incorporated into fish hard parts represents an innovative technique for this task. Otolith chemistry has been used to describe fish stock structure, migratory behaviour and to support the management of several species. We used otolith chemistry to study the dispersal and population structure of Tench (Tinca tinca), an invader in the St Lawrence River. Tench movements throughout the invaded portion of the system were reconstructed using a Random Forests Algorithm. The results showed that, despite the presumed limited dispersal capacity of the species, Tench are capable of extensive migratory movements (up to 250 km). The variability in migratory patterns among individuals, including both short- and long-distance movements, supports a stratified diffusion. Such a strategy may explain the successful invasion of Tench in the St Lawrence River ecosystem. Our study represent a flexible framework for the study of Tench ecology in its invaded and native range, as well as for other freshwater invasive fishes.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.019 |
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".