A statistical model for calibration and computation of detection and quantification limits for low copy number environmental DNA samples
Bibliographic record
Abstract
Abstract Environmental DNA (eDNA) has been increasingly utilized by academic, industry, and government groups for environmental monitoring due to its efficiency in regards to both time and cost, as well as non‐invasiveness to target organisms, and reduced dependency on trained biologists for sample collection. The methods typically employ quantitative real‐time polymerase chain reaction (qPCR) to detect the presence of a given organism's DNA in a sample. Currently, there is a drive to use qPCR data to infer biomass or abundance by quantitating the copy number or concentration of a given target gene fragment in a sample, which is often very dilute. Before eDNA can be fully accepted as an environmental decision‐making tool, however, certain aspects of the methods require standardization, including the quantification of target DNA in low copy number samples. Models that are not able to properly make use of data from highly dilute samples are severely hampered in their definitions of the limits of detection and quantification at the lower end of the detection curve. We propose a statistical model for a standard curve that relates the number of qPCR‐detected technical replicates to the copy number in the case of low copy number samples. Likelihood methods are used to estimate the parameters of the model and we derive inverse regression estimates together with their standard errors. Limits of copy number detection and quantification, and their confidence intervals are derived using a well‐accepted statistical approach thus providing a more broadly applicable and robust method for reporting eDNA abundance into the low copy number range. The method is illustrated using experimental results from multiple laboratories.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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; 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".