Dataset to: Deforestation for agriculture leads to soil warming and enhanced litter decomposition in subarctic soils
Bibliographic record
Abstract
Deforestation for agriculture leads to soil warming and enhanced litter decomposition in subarctic soils T. Peplau, C. Poeplau, E. Gregorich, J. Schroeder This repository contains a dataset of soil temperature, soil parameters, farm management and additional site informations. Soil_temperature_data_Yukon.zip: Temperature data from different farms across the Yukon. Each .xlsx file contains data from one temperature logger that logged soil temperature every 2 hours. The individual sheets are named in the following scheme: Farm_landuse_depth.xlsx Farm contains two letters corresponding to the identifier in the soil data set landuse contains either F ("Forest"), CM ("Cropland / Market Garden") or G ("Grassland") Depth is either 10 cm or 50 cm teabags.csv contains raw data about the initial weight of the teabags buried, their location and their weight after two years in the soil tea_decomposition contains the mean decomposition (n=3) of the tabags from each plot and corresponding temperature statistics, based on the logger data Soil_I_IV.csv contains soil parameters from soil samples at 0-10 cm and 40-60 cm site_data_R.csv contains geographical information and soil data that has only been measured once per site
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 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".