Improved protocols to accelerate the assembly of <scp>DNA</scp> barcode reference libraries for freshwater zooplankton
Bibliographic record
Abstract
Currently, freshwater zooplankton sampling and identification methodologies have remained virtually unchanged since they were first established in the beginning of the XX century. One major contributing factor to this slow progress is the limited success of modern genetic methodologies, such as DNA barcoding, in several of the main groups. This study demonstrates improved protocols which enable the rapid assessment of most animal taxa inhabiting any freshwater system by combining the use of light traps, careful fixation at low temperatures using ethanol, and zooplankton-specific primers. We DNA-barcoded 2,136 specimens from a diverse array of taxonomic assemblages (rotifers, mollusks, mites, crustaceans, insects, and fishes) from several Canadian and Mexican lakes with an average sequence success rate of 85.3%. In total, 325 Barcode Index Numbers (BINs) were detected with only three BINs (two cladocerans and one copepod) shared between Canada and Mexico, suggesting a much narrower distribution range of freshwater zooplankton than previously thought. This study is the first to broadly explore the metazoan biodiversity of freshwater systems with DNA barcodes to construct a reference library that represents the first step for future programs which aim to monitor ecosystem health, track invasive species, or improve knowledge of the ecology and distribution of freshwater zooplankton.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".