Biodiversity inventory of the grey mullets (Actinopterygii: Mugilidae) of the Indo‐Australian Archipelago through the iterative use of DNA‐based species delimitation and specimen assignment methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
DNA barcoding opens new perspectives on the way we document biodiversity. Initially proposed to circumvent the limits of morphological characters to assign unknown individuals to known species, DNA barcoding has been used in a wide array of studies where collecting species identity constitutes a crucial step. The assignment of unknowns to knowns assumes that species are already well identified and delineated, making the assignment performed reliable. Here, we used DNA-based species delimitation and specimen assignment methods iteratively to tackle the inventory of the Indo-Australian Archipelago grey mullets, a notorious case of taxonomic complexity that requires DNA-based identification methods considering that traditional morphological identifications are usually not repeatable and sequence mislabeling is common in international sequence repositories. We first revisited a DNA barcode reference library available at the global scale for Mugilidae through different DNA-based species delimitation methods to produce a robust consensus scheme of species delineation. We then used this curated library to assign unknown specimens collected throughout the Indo-Australian Archipelago to known species. A second iteration of OTU delimitation and specimen assignment was then performed. We show the benefits of using species delimitation and specimen assignment methods iteratively to improve the accuracy of specimen identification and propose a workflow to do so.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it