Ten simple rules to bridge ecology and palaeoecology by publishing outside palaeo-ecological journals
Bibliographic record
Abstract
Due to a specialised methodology, palaeoecology is often regarded as a separate field from ecology even though it is essential to understand long-term ecological processes that have shaped ecosystems that ecologists study and manage. Even though advances in ecological modelling, sample dating, and proxy-based reconstructions have enabled direct comparison of palaeoecological data with neo-ecological data, most of the scientific knowledge derived from palaeoecological studies remains siloed. We have surveyed a group of palaeo-researchers with experience in crossing the divide between palaeoecology and neo-ecology, with the goal to provide a set of Ten Simple Rules to publish your palaeo-ecological research in non-palaeo journals. Our ten rules are divided into the preparation phase, writing phase, and finalising phase when the article is submitted to the target journal. These rules provide a suite of strategies, including improved and early-on networking and effective collaborations, transmitting results in a more efficient and cross-disciplinary manner, and integrating concepts and methodologies that appeal to ecologists and a wider readership. Following these Ten Simple Rules can help palaeoecologists ensure that their work is disseminated and understood by mainstream ecological scientists. Although this article shows primarily examples of how palaeoecological studies were published in journals for a broader audience, the rules would apply to anyone who aims to publish outside specialised journals.
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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 | 0.158 | 0.281 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.041 | 0.016 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.017 |
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".