Characterization of forage fish and invertebrates in the Northwestern Hawaiian Islands using fatty acid signatures: species and ecological groups
Bibliographic record
Abstract
The fat content and fatty acid (FA) composition of 100 species of fishes and invertebrates (n = 2190) that are potential key forage species of the critically endangered monk seal in the Northwestern Hawaiian Islands were determined. For analysis, these species were classified into 47 groups based on a range of shared factors such as taxonomy, diet, ecological subsystem, habitat, and commercial interest. Hierarchical cluster and discriminant analyses of the 47 groups using 15 major FAs revealed that groups of species with similar FA composition associated into 5 functional groups: herbivores, planktivores, carnivores (which also included piscivores and omnivores), crustaceans, and cephalopods. Discriminant analyses performed on the 4 main functional groups separately revealed that herbivores, planktivores, and crustaceans could be readily differentiated on the basis of their FA signatures, with 97.7, 87.2, and 81.5% of individuals correctly classified, respectively. Classification success was lower within the carnivores (75.5%), which indicates that some groups of carnivorous species likely exhibit highly similar diets and/or ecology, rendering their FA signatures harder to differentiate. Despite a relatively broad overlap, most groups were reasonably well classified (> 75% success). Five groups had < 60% classification success; these taxa were better explained by separately considering individual factors such as species, diet, or ecology rather than the full range of shared factors. The agreement of the FA signatures with ecologically based groupings indicates they can be used to characterize the fish and invertebrate community in this highly complex subtropical ecosystem. These data provide crucial information on the distinctions and overlaps in the diets of reef fish and invertebrates.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".