Ecology in the age of <scp>DNA</scp> barcoding: the resource, the promise and the challenges ahead
Bibliographic record
Abstract
Ten years after DNA barcoding was initially suggested as a tool to identify species, millions of barcode sequences from more than 1100 species are available in public databases. While several studies have reviewed the methods and potential applications of DNA barcoding, most have focused on species identification and discovery, and relatively few have addressed applications of DNA barcoding data to ecology. These data, and the associated information on the evolutionary histories of taxa that they can provide, offer great opportunities for ecologists to investigate questions that were previously difficult or impossible to address. We present an overview of potential uses of DNA barcoding relevant in the age of ecoinformatics, including applications in community ecology, species invasion, macroevolution, trait evolution, food webs and trophic interactions, metacommunities, and spatial ecology. We also outline some of the challenges and potential advances in DNA barcoding that lie ahead.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.031 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".