Dataset for "Environmental drivers of increased ecosystem respiration in a warming tundra"
Bibliographic record
Abstract
Data for Nature manuscript titled “Environmental drivers of increased ecosystem respiration in a warming tundra” Corresponding author Dr. Sybryn Maes – sybryn.maes@gmail.com Github contains all R scripts on https://github.com/mjalava/tundraflux Part A. Meta-analysis The bold names refer to scripts (see the Github repository https://github.com/mjalava/tundraflux) and names in italics refer to files in this repository df_0 -Study design Figure 1 and Extended Fig. 1 from main text df_1a -Effect size calculations of response (ER) -Links to df_1.csv file with raw flux and environmental data -Only the experiments that state ‘Open Access’ in the excel file Authors_Datasets (sheet 2). For experiments stating ‘Available Upon Request’, you need to contact the authors for the -raw flux data. df_1b -Effect size calculations of environmental drivers -Links to df_1.csv file with raw flux data data (see above) and Dataset_ID.csv (this file includes all dataset IDs to merge the drivers into one dataframe) df_2a-f -Meta-analysis (2a) and meta-regression models (2b-f) (ER, N=136) -Links to df_2.csv file with effect size data and context-dependencies and Forestplot_horiz_weights_fig.csv (this file includes the mean pooled Hedges SMD as well as the individual dataset Hedges SMD to plot figure 2) -Contains code for Figs. 2-4 and Extended Figs 2-3 df_3 -Meta-regression for experimental warming duration -Contains code for Fig. 5 df_4a -Effect size calculations of autotrophic-heterotrophic respiration partitioning (Ra, Rh, N=9) -Links to df_3.csv file with raw partitioning data of subset experiments (output file df_4.csv) df_4b -Sub-meta-analysis models (ER, Ra, Rh) -Links to df_4.csv (input file) NOTES · All additional input files for the meta-analysis R-scripts are included within the folders. · ER, Ra, Rh = ecosystem, autotrophic, and heterotrophic respiration · N = sample size (number of datasets) Part B. Upscaling results For upscaling, the input data is described in the code files (see the Github repository) and the accompanying Readme.txt. percentageChangeResp_tundraAlpine.tif: modelled change in respiration baseResp_tundraAlpine.tif: baseline respiration (calculated from the data from literature) modResp_tundraAlpine.tif: modelled respiration after warming (our calculations: (percentageChangeResp_tundraAlpine+1) * baseResp_tundraAlpine) changeResp_tundraAlpine.tif: modResp-baseResp standError_tundraAlpine.tif: standard error of modelled respiration ( standError_tundraAlpine_onlyDataUncertainty.tif: standard error of modelled respiration where only data uncertainty is taken into account
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".