Celebrating 25 years of advances in micropalaeontology: a review
Bibliographic record
Abstract
Abstract. INTRODUCTION (F. JOHN GREGORY)To commemorate the publication of the 25th Volume of the Journal of Micropalaeontology, the first issue of which came out in 1982, this celebratory review article was commissioned. Officers of each TMS Group (Ostracod, Foraminifera, Palynology, Nannofossil, Microvertebrate and Silicofossil) were requested to reflect over the last 25 years and assess the major advances and innovations in each of their disciplines. It is obvious from the presentations that all Groups report that research has moved on from the basic, but essential descriptive phase, i.e. taxonomy and establishing biostratigraphies, to the utilization of new technologies and application to issues of the day such as climate change and global warming. However, we must not lose sight of the fact that the foundation of micropalaeontology is observation and the building block for all these new and exciting innovations and developments is still good taxonomy. Briefly, the most obvious conclusion that can be drawn from this review is that micropalaeontology as a science is in relatively good health, but we have to ensure that the reported advancements will sustain and progress our discipline. There is one issue that has not really been highlighted in these contributions – we need to make sure that there are enough people being trained in micropalaeontology to maintain development. The last 25 years has seen a dramatic decrease in the number of post-graduate MSc courses in micropalaeontology. For example, in the UK, in the 1980s and early 1990s there were five specific MSc courses to choose . . .
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".