Industrielle Mikropaläontologie / Industrial Micropaleontology
Bibliographic record
Abstract
In den 1920er Jahren revolutionierten die Wissenschaftlerinnen Esther Applin, Alva Ellisor und Hedwig Kniker die Mikropaläontologie. Sie arbeiteten für Ölfirmen. In diesem Rahmen gelang es ihnen, das enorme Potential mikropaläontologischer Analysen für die Stratigrafie und die Erdölgeologie nachzuweisen. Tiere als Objekte? ist eine Online-Publikation von Wissenschaftler:innen des Museums für Naturkunde Berlin, des Berliner Zoos und der Humboldt-Universität zu Berlin, herausgegeben von Ina Heumann und Tahani Nadim. Die Publikation ist Teil des vom BMBF-geförderten Forschungsprojekts "Tiere als Objekte. Zoologische Gärten und Naturkundemuseum in Berlin, 1810 bis 2020". In the 1920s, scientists Esther Applin, Alva Ellisor, and Hedwig Kniker revolutionised micropaleontology. They were employed by oil companies and discovered important applications for stratigraphy and petroleum geology. Animals as Objects? is an online publication by researchers from the Museum für Naturkunde Berlin, the Zoo Berlin, and the Humboldt-Universität zu Berlin, edited by Ina Heumann and Tahani Nadim. It was funded by the BMBF as part of the research project "Animals as Objects. Zoological Gardens and Natural History Museum in Berlin, 1810 to 2020".
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".