Paleo-diatom composition from Santa Barbara Basin deep-sea sediments: a comparison of <i>18S-V9</i> and <i>diat-rbcL</i> metabarcoding vs shotgun metagenomics
Why this work is in the frame
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Bibliographic record
Abstract
Sedimentary ancient DNA (sedaDNA) analyses are increasingly used to reconstruct marine ecosystems. The majority of marine sedaDNA studies use a metabarcoding approach (extraction and analysis of specific DNA fragments of a defined length), targeting short taxonomic marker genes. Promising examples are 18S-V9 rRNA (~121-130 base pairs, bp) and diat-rbcL (76 bp), targeting eukaryotes and diatoms, respectively. However, it remains unknown how 18S-V9 and diat-rbcL derived compositional profiles compare to metagenomic shotgun data, the preferred method for ancient DNA analyses as amplification biases are minimised. We extracted DNA from five Santa Barbara Basin sediment samples (up to ~11 000 years old) and applied both a metabarcoding (18S-V9 rRNA, diat-rbcL) and a metagenomic shotgun approach to (i) compare eukaryote, especially diatom, composition, and (ii) assess sequence length and database related biases. Eukaryote composition differed considerably between shotgun and metabarcoding data, which was related to differences in read lengths (~112 and ~161 bp, respectively), and overamplification of short reads in metabarcoding data. Diatom composition was influenced by reference bias that was exacerbated in metabarcoding data and characterised by increased representation of Chaetoceros, Thalassiosira and Pseudo-nitzschia. Our results are relevant to sedaDNA studies aiming to accurately characterise paleo-ecosystems from either metabarcoding or metagenomic data.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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