Feeding ecology and niche overlap of Lake Ontario offshore forage fish assessed with stable isotopes
Bibliographic record
Abstract
The forage fish communities of the Laurentian Great Lakes continue to experience changes that have altered ecosystem structure, yet little is known about how they partition resources. Seasonal, spatial, and body size variation in δ13C and δ15N was used to assess isotopic niche overlap and resource and habitat partitioning among the five common offshore Lake Ontario forage fish species (n = 2037; alewife (Alosa pseudoharengus), rainbow smelt (Osmerus mordax), round goby (Neogobius melanostomus), and deepwater (Myoxocephalus thompsonii) and slimy (Cottus cognatus) sculpins). Round goby had the largest isotopic niche (6.1‰2, standard ellipse area), followed by alewife (3.4‰2), while rainbow smelt, slimy sculpin, and deepwater sculpin had the smallest and similar niche size (1.7‰2–1.8‰2), with only the sculpin species showing significant isotopic niche overlap (>63%). Stable isotopes in alewife, round goby, and rainbow smelt varied with location, season, and size, but did not vary in the sculpin species. Lake Ontario forage fish species have partitioned food and habitat resources, and non-native alewife and round goby have the largest isotopic niche, suggestive of a boarder ecological niche, and may contribute to their current high abundance.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".