Data from: Discrimination of grasshopper (Orthoptera: Acrididae) diet and niche overlap using next-generation sequencing of gut contents
Bibliographic record
Abstract
Species of grasshopper have been divided into three diet classifications based on mandible morphology: forbivorous (specialist on forbs), graminivorous (specialist on grasses), and mixed feeding (broad-scale generalists). For example, Melanoplus bivittatus and Dissosteira carolina are presumed to be broad-scale generalists, Chortophaga viridifasciata is a specialist on grasses, and Melanoplus femurrubrum is a specialist on forbs. These classifications, however, have not been verified in the wild. Multiple specimens of these four species were collected, and diet analysis was performed using DNA metabarcoding of the gut contents. The rbcLa gene region was amplified and sequenced using Illumina MiSeq sequencing. Levins' measure and the Shannon–Wiener measure of niche breadth were calculated using family-level identifications and Morisita's measure of niche overlap was calculated using operational taxonomic units (OTUs). Gut contents confirm both D. carolina and M. bivittatus as generalists and C. viridifasciata as a specialist on grasses. For M. femurrubrum, a high niche breadth was observed and species of grasses were identified in the gut as well as forbs. Niche overlap values did not follow predicted patterns, however, the low values suggest low competition between these species.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".