Data for "Climate outweighs human effects on vegetation properties during the early-to-mid Holocene"
Bibliographic record
Abstract
Data for analyses used in the PastHumanImpact project published in a manuscript "Climate outweighs human effects on vegetation properties during the early-to-mid Holocene". All R code and workflows to reproduce the data analyses and figures are available at HOPE-UIB-BIO/PastHumanImpact.Usage:Each zip file should be extracted and placed into the `Data` folder, following the README file in the mentioned GitHub repo.❗The data is under a CC BY-NC-ND licence, which requires approval from data owners before use (read here for more details). Specifically, please contact Xianyong Cao (xcao@itpcas.ac.cn) to share the Asian fossil pollen data.❗Citation:If you want to use the data, please cite the mentioned paper "Felde et al. Climate outweighs human effects on vegetation properties during the early-to-mid Holocene". DOI: 10.21203/rs.3.rs-4692574/v1
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.049 |
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".