Data used in the article "Climate is stronger than you think: Exploring functional planting and TRIAD zoning for increased forest resilience to extreme disturbances"
Bibliographic record
Abstract
The dataset is separated into 3 sections : - The first section contains the data used to calibrate certain parameters of LANDIS-II : in particular, the data necessary to calibrate the Base Fire extension in our study area with climate change. - Users can find the files to launch the calibration scenario for the parameters of Base Fire, and a table file describing the process of empirical determination of those parameters via repeated calibration simulations (Empirical determination of the fire parameters_CC_v2.ods) - The second section contains all of the scenarios folders with all of the parameters files to launch all of the simulations made for the study. Users can upload those files on the clusters of the Digital Research Alliance of Canada to replicate our results if needed, using the job_script_python_robot_v2.sh script to launch a job on the cluster. The job will use the Python_watcher_bot.py script, which will monitor if simulations are running as needed. - A WARNING : Your results will be very slightly different as the files that we have re-generated for this figshare repository have different pseudorandom seed values for each replicate. The differences shouldn't be noticable. - The third section contains all of the files resulting from the simulation and of the analysis of the simulation outputs, along with the files necessary to produce the figures of the article . The origin of the data is described in the supplementary material of the article, and in our previous study (Hardy C, Messier C, Boulanger Y, Cyr D, Filotas É. Land sparing and sharing patterns in forestry: exploring even-aged and uneven-aged management at the landscape scale. Landsc Ecol. 2023 Nov 1;38(11):2815–38.).
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 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.055 |
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".