Evolutionary Ecology of Kerguelen Islands Colonization by Introduced Salmonids: SALMEVOL 1041-2 project
Bibliographic record
Abstract
The present report is a synthesis of all studies conducted around the long-term ecological research(LTER) monitoring of introduced salmonid species in the sub Antarctic Kerguelen islands over the 2015-2020 period, within the SALMEVOL-2 project. The monitoring encompasses the history of eight species,five of which are still present in Kerguelen, the data and collections spanning five decades and tenthsof rivers. Based on this monitoring, but also thanks to various field experiments, we have undertakento study the evolutionary ecology of these species, using the invasive Brown trout as flagship model,under the premise that the Kerguelen situation, where rivers were previously void of any fish species,could be an anticipation lab of the situation developing at the poles due to climate change. Ourfindings pertain to life history traits such as individual growth, migration between freshwater andmarine ecosystems, microbiomes, but also proximal and ultimate mechanisms of adaptation in relationto the local environment. We also begin to investigate how the expanding metapopulation structure,resulting from multiple invasion events, may affect life history traits evolution.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".