Lake Ladoga: The Coastal History of the Greatest Lake in Europe, edited by Maria Lähteenmäki and Issac Land
Bibliographic record
Abstract
REVIEWS • 291and immediately go north to help or be involved in this Arctic paleolimnology community effort.So, what kind of printed work is this, finally?A textbook?Not really.A memoir?Not only.A fascinating journeyboth spatially and temporally-into a truly interdisciplinary field of research, told by one of its founders?Certainly.I would highly recommend it to any graduate student starting a project in any field of physical geography, biology, ecology, archeology, or anything involving freshwater ecosystem dynamics at different spatial and temporal scales.More generally, the book is quite relevant for Arctic researchers wishing to know more about paleolimnology and for paleolimnologists interested in Arctic fieldwork and the challenges specific to high-latitude environments.I would add that it can also be of great interest to science historians studying the development of novel ideas (see the many examples of transdisciplinary coffee discussions in the book) and the patient and time-consuming lab analyses (and controversies-you need those, too).This is a book I wish I had read twentyish years ago, at the start of my tortuous scientific career.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".