Characterizing the diet and habitat niches of coastal fish populations in the Beaufort Sea Tarium Niryutait Marine Protected Area
Bibliographic record
Abstract
To evaluate the niche of coastal fish populations in the Beaufort Sea, stable isotopes (SI) and fatty acids (FA) were used to characterize species-specific niches, niche overlaps and resource partitioning (nicheROVER) of the Shingle Point fish populations. Fishes were grouped into three isotopic groups: marine, coastal, and freshwater (Ward’s clustering analysis), and five dietary groupings (using FA), where benthic feeding strategies were prevalent (correspondence analysis). Niche metrics were used to evaluate if total mercury (THg) could contribute complementary trophic information (residual permutation procedure (RPP)). Three THg groups (high, intermediate, low) were identified (boxplot analysis). High THg was identified in high trophic and benthic feeders, high THg ranges were observed in species with large niche sizes, high trophic feeding, and freshwater influences (RPP). The bioavailability of freshwater introduced THg to marine biota was assessed, however further research needs to be performed. Combining dietary indicators SI, FA, and THg, allowed for the characterization of the diet and habitat use of coastal fish populations, better understanding of the niches of these species, and developed baseline information for future monitoring in an MPA, as climate change continues to effect the Beaufort coastal environment.
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 | 0.000 | 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".