Bibliographic record
Abstract
At this point it is appropriate, if not necessarily wise, to attempt a brief view into the future. By its very nature, scientific research is not usually kind to those who would attempt to plan it, or even forecast its future direction. Be this as it may, it seems to us that some of the immediate future directions in applied diatom studies seem almost foreordained. It is very clear that a good deal of effort needs to be devoted to the formalities of taxonomy and nomenclature, which have been sadly neglected for the past century. Great strides have been made very recently in the alpha level taxonomy of diatoms (e.g., Lange-Bertalot & Metzelin, 1996). It has also become much more common for diatomists to document their work in published iconographs (e.g., Douglas & Smol, 1993; Cumming et al., 1995) followed by deposition of properly vouchered material from major studies. Even more promising, the application of modern systematic techniques to diatoms (e.g., Kociolek & Stoermer, 1989; Kociolek et al., 1989; Theriot & Stoermer, 1984; Williams, 1985) is becoming more and more established. However, it is also true that relatively few diatomists are formally trained systematists. The very increase in interest in diatom taxonomy, particularly that part fueled by practical applications, has left behind it a virtual morass of nomenclatorial problems. Unfortunately, many well-intentioned attempts to alleviate the situation have resulted in inappropriate synonymies, conservations, and circumscriptions, which only serve to further complicate it.
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.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.069 | 0.016 |
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".