Data on insect biodiversity in a Chinese potato agroecosystem from DNA metabarcoding
Bibliographic record
Abstract
Potato (Solanum tuberosum) is a staple crop important in global food security. As a leading potato producer, China faces significant challenges from insect pest infestations that compromise yield and quality. However, insect communities within Chinese potato fields remain poorly characterized. This study aimed to explore insect diversity in potato fields in Yunnan Province. From autumn 2021 to summer 2022, five Malaise traps were strategically deployed to capture insect samples. In total, 245 samples were collected over 49 weeks, and DNA metabarcoding was performed on bulk samples. The generated sequences were curated and analyzed using the Barcode of Life Data System and the Multiplex Barcode Research and Visualization Environment. The analysis assigned sequences to 1,688 Barcode Index Numbers (BINs) as species proxies derived from the Global Insecta Library, along with 166 BINs from the China Insecta dataset. This research provides valuable insights for barcoding local biodiversity and developing regional reference libraries and presents a comprehensive dataset of insect biodiversity within potato agroecosystems, encompassing 1,707 BINs linked to known insect taxa.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| 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 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".