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Record W2139504386 · doi:10.1144/jm.25.2.97

Celebrating 25 years of advances in micropalaeontology: a review

2006· review· en· W2139504386 on OpenAlexaff
F. John Gregory, Howard A. Armstrong, Ian Boomer, Rainer Gersonde, Ian H. Harding, Jens O. Herrle, David Lazarus, Daniela N. Schmidt, J. Schoenfeld, J. R. Young

Bibliographic record

VenueJournal of Micropalaeontology · 2006
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEngineering ethicsTaxonomy (biology)HistoryPolitical scienceEnvironmental ethicsPsychologyData scienceLibrary scienceEngineeringEcologyComputer scienceBiologyPhilosophy

Abstract

fetched live from OpenAlex

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 . . .

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.322
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2006
Admission routes1
Has abstractyes

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