Applying DNA barcoding to red macroalgae: a preliminary appraisal holds promise for future applications
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
Marine macroalgae, especially the Rhodophyta, can be notoriously difficult to identify owing to their relatively simple morphology and anatomy, convergence, rampant phenotypic plasticity, and alternation of heteromorphic generations. It is thus not surprising that algal systematists have come to rely heavily on genetic tools for molecular assisted alpha taxonomy. Unfortunately the number of suitable marker systems in the three available genomes is enormous and, although most workers have settled on one of three or four models, the lack of an accepted standard hinders the comparison of results between laboratories. The advantages of a standard system are obvious for practical purposes of species discovery and identification; as well, compliance with a universal marker, such as cox1 being developed under the label 'DNA barcode', would allow algal systematists to benefit from the rapidly emerging technologies. Novel primers were developed for red algae to PCR amplify and sequence the 5' cox1 'barcode' region and were used to assess three known species-complex questions: (i) Mazzaella species in the Northeast Pacific; (ii) species of the genera Dilsea and Neodilsea in the Northeast Pacific; and (iii) Asteromenia peltata from three oceans. These models were selected because they have all caused confusion with regards to species number, distribution, and identification in the field, and because they have all been studied with molecular tools. In all cases the DNA barcode resolved accurately and unequivocally species identities and, with the enhanced sampling here, turned up a variety of novel observations in need of further taxonomic investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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