Dataset for "Assmann et al. High variation in the surface extent of freshwater ponds creates dynamic Arctic tundra landscapes in the lowlands of Eastern Siberia"
Bibliographic record
Abstract
Overview Time-series of true colour drone imagery (RGB) and associated digital surface models (DSM) for three tundra landscapes. These data are required to replicate the analysis in "Assmann et al. High variation in the surface extent of freshwater ponds creates dynamic Arctic tundra landscapes in the lowlands of Eastern Siberia" (link to preprint). Location: Kytalyk National Park, Russian FederationTime-span: 2014-2021 (near-annual)What: drone derived RGB raster imagery and digital surface models (.tif)Ground-sampling distance: 12 cmProjection: UTM55N (EPSG: 32655) The data are intended to be integrated into the GitHub repository (https://github.com/jakobjassmann/pond_project, also archived on Zenodo: https://doi.org/10.5281/zenodo.17318379). Instructions below. For further detail on data generation and flight information see manuscript and supplementary materials, as well as information provided on the GitHub code repository. Site names Please note that the abbreviated site names used here and in the code repository differ from those used in the manuscript. dataset / code manuscript cbh "high" tlb "med" rdg "low" Integration with code repository To integrate the data with the code repository: Clone the code repository to your local machine. (https://github.com/jakobjassmann/pond_project, also archived on Zenodo: https://doi.org/10.5281/zenodo.17318379) Download the compressed data in this repository. Extract archive contents as follows: cbh.7z -> data/drone_data/cbh/ tlb.7z -> data/drone_data/tlb/ rdg.7z -> data/drone_data/rdg/ Citation When uisng this data in the context of scientific work, we kindly ask to cite the accompanying manuscript (below) in addtion to the dataset citation provided by Zenodo. At the time of writing the final manuscript is in press, when referencing the dataset before publication, please cite the following preprint: Assmann, J.J., Akandil, C., Plekhanova, E., Le Moigne, A., Karsanaev, S.V., Maximov, T.C., Schaepman-Strub, G. (2025). Freshwater ponds create highly dynamic Arctic tundra landscapes. Preprint on Research Square. https://doi.org/10.21203/rs.3.rs-5581925/v1 Acknowledgements We would like to thank all drone-pilots and field team members who contributed to the data collection during the field seasons 2014 – 2021, including Inge Grünberg (nee Juszak), Maitane Iturrate-Garcia and Vitalii Zemlianskii. This study was supported by the Swiss National Science Foundation (grant no. 178753) and the University Research Priority Program on Global Change and Biodiversity of the University of Zurich.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.120 | 0.083 |
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".