An analysis of metadata reporting in freshwater environmental DNA research calls for the development of best practice guidelines
Bibliographic record
Abstract
Abstract As environmental DNA (eDNA) becomes more widely used in research, it becomes increasingly important to have a standard set of reporting guidelines for metadata. The unique properties of eDNA combined with the physical characteristics of the surrounding environment produce highly varied sampling conditions which can influence how an organism is detected. There are also various ways of quantifying and identifying species using eDNA, from sampling and filtering methods to extraction and genetic analysis. It is important to report sufficient metadata to account for this variability and allow for replication of the study. We conducted a systematic review of 160 eDNA studies to determine which data are reported and to assess whether these studies can be replicated. Focusing solely on freshwater studies, we developed a rubric to evaluate each study on 53 criteria based on previous analyses of eDNA research. We found a trend in the data suggesting better reporting at a broad scale, and decreased reporting as categories become more specific. Many of the metrics found to be insufficiently reported are essential to replicability. Our goal is to identify gaps in metadata reporting and develop a framework for developing standard reporting guidelines for eDNA studies.
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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 | 0.614 | 0.823 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.037 | 0.040 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".