Peer Review #2 of "Using DNA metabarcoding to assess insect diversity in citrus orchards (v0.2)"
Bibliographic record
Abstract
Background.DNA metabarcoding is rapidly emerging as a cost-effective approach for large-scale biodiversity assessment and pest monitoring.The current study employed metabarcoding to assess insect diversity in citrus orchards in Ganzhou city, Jiangxi, China in both 2018 and 2019.Insects were sampled using Malaise traps deployed in three citrus orchards producing a total of 43 pooled monthly samples.Methods.The Malaise trap samples were sequenced following DNA metabarcoding workflow.Generated sequences were curated and analyzed using two cloud databases and analytical platforms, the Barcode of Life Data System (BOLD) and Multiplex Barcode Research And Visualization Environment (mBRAVE).Results.These platforms assigned the sequences to 2,141 Barcode Index Numbers (BINs), a species proxy .Most (63%) of the BINs were shared among the three sampling sites while BIN sharing between any two sites did not exceed 71%.Shannon diversity index (H′) showed a similar pattern of BIN assortment at the 3 sampling sites.Beta diversity analysis by Jaccard similarity coefficient (J) and Bray-Curtis distance matrix (BC) revealed a high level of BIN similarity among the three sites (J = 0.67-0.68;BC = 0.19 -0.20).Comparison of BIN records against all those on BOLD made it possible to identify 40% of the BINs to a species, 57% to a genus, 97% to a family and 99% to an order.BINs which received a species match on BOLD were placed in one of four categories based on this assignment: pest, parasitoid, predator, or pollinator.As this study provides the first baseline data on insect biodiversity in Chinese citrus plantations, it is a valuable resource for research in a broad range of areas such as pest management and monitoring beneficial insects in citrus gardens.
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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 | 0.014 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.351 | 0.216 |
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".