144 Use of Fourier Transform Infrared (FT IR) Diffuse Reflectance Spectroscopy and Hamming Distances to Study the Phycocolloid Chemosystematics of the Red Algae (Rhodophta)
Bibliographic record
Abstract
The use of red algal polysaccharide cell wall structures as taxonomic indicators has been a recurring theme in the literature for over 58 years. Traditionally the polymeric phycocolloids of the red algal cell walls have been classified into definite structures (carrageenans and agars) based on the phycocolloids present. However, this method has some obvious shortcomings, such as its inability to assign hybrid structures (carragars), or the appearance of agarocolloids in traditional ‘carrageenophytes’. Thus, there is a need to develop an unbiased method of examining phycocolloid structures without assigning them names. Fourier transform infrared diffuse reflectance spectroscopy was used to analyze the spectra of a large number of red algal species, belonging to several orders. The spectra for each species resulted in a series of ‘peaks’ corresponding to certain wavenumbers (cm−1), which in term correspond to phycocolloid bonds. On the basis of shared/similar peaks, irrespective of their combinations to describe polymeric units, a series of matrices were developed for each family. These were coded as a 0 and 1, indicating the presence or absence of a peak, respectively. Using the concept of vector addition and Hamming distances, a methodology was formulated for analyzing ordinal and familial level groupings. Initial analysis on 173 species, representing 39 families and 12 orders of the Rhodophyta, proved to be promising. Some results corroborated previous studies, while others proved to be informative and raised the need for further chemosystematic studies. This provides the impetus for continuing to develop this method as a contribution to red algal systematics.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".