Dataset: Long-term geothermal warming reduced stocks of carbon but not nitrogen in a subarctic forest soil
Bibliographic record
Abstract
The files stored in this repository contain data and additional information for the study "Long-term geothermal warming reduced stocks of carbon but not nitrogen in a subarctic forest soil" by Tino Peplau, Julia Schroeder, Edward Gregorich and Christopher Poeplau. climate-data-takhini.txt: Contains a dataset with climate data used for Figure 1a and b. The data was downloaded from https://climatedata.ca/download/ as single variables and later on put into this single file. degree_days.xlsx: Contains soil temperature data with according calculation of cumulative degree days. temperature.xlsx: Contains raw data of soil temperature teabags_HS.xlsx: Contains information about all 24 buried teabags. The table contains 6 columns: 1)"sample" gives the individual name of the sample. 2) "rep" is the replication at each plot 3) "plot" is the plot, according to the soil warming intensity 4) "depth" is the depth at which the teabag was buried 5) "weight_start" is the weight of tea before at start of the experiment 6) "weight_end" ist the weight of the tea after one year of burial HS_data_final.xlsx: Contains all data of the soil samples. It is divided into two sheets: "sample_data": Provides information about every single soil sample, including chemical data, bulk density, organic and inorganic carbon, nitrogen and fractions. "plot_data": Provides a summary of the data for every soil core (repetition) and plot, including mass corrected SOC and N stocks of the whole profile, topsoil and subsoil.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.056 | 0.040 |
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".