The pace of life for forest trees
Bibliographic record
Abstract
The input data needed to replicate the analyses for the paper titled: 'The pace of life for forest trees'. Readme file for the paper titled: The pace of life for forest trees. Bialic-Murphy, et al 2024 --------------------------------------------------------- Data: -------------------------------------------------------------- Initial inputs TreeMort_TreeData.Rdata --Cannot share original data due to third-party data restrictions. The included subset are the data from networks that are fully open access. The other datasets can be accessed by submitting a data request to the networks. See DMA statement for more details hex_250k.csv --Contains climate data at grid cell level. Data coordinates are not provided due to sensitive species. Outputs from Script 1 and grid-level input data needed to replicate models and analysis, i.e., Script 2 - 6 TreeMort_Growth_1yr_woGrid.Rdata --Growth function parameters TreeMort_sigmag_1yr_woGrid.Rdata --Variance estimates for growth function TreeMort_Survival_1yr_woGrid.Rdata --Survival function parameters Min_Max_Size.Rdata --Species size characteristics Forest network data that included sensitive species and indigenous data sovereignty were not included in this paper, but these data are available for research purposes by submitting a data request to the following networks: Alberta Forestry Division forestry.alberta.ca, Saskatchewan Minister of Environment https://geohub.saskatchewan.ca, British Columbia https://www2.gov.bc.ca/gov, Placette-échantillon permanente Données Québec donneesquebec.ca, ForestGeo https://forestgeo.si.edu, and ForestPlots. -------------------------------------------------------------- R code associated needed to replicate the results of this study are available at https://github/Lalasia/pace_of_life.com -------------------------------------------------------------- 1_Fit_Vital_R --Load data and fit vital rate functions. 2_IPM_4_HPC.R --Load vital rate parameter estimates, set up IPMs, calculate LHTs 2_IPM_4_HPC.sh --Sets up 2_IPM_4_HPC.R to run on an HPC. 3_IPM_postProc.R --Compiles all runs generated from 2_IPM_4_HPC.R. Creates data files for subsequent scripts 4_Clusters_analysis.R --Runs cluster analysis and generates figures 5_Bayesian_model.R --Runs analysis of environmental effects on LHTs 6_Hull_volume.R --Calculates the convex hull of LHTs and analyses patterns Error_Checks --Contains modified versions of 2_IPM_4_HPC.sh and 2_IPM_4_HPC.R (See Note 2 below). --Run these scripts to recreate our analyses and results. --NOTE 1 | The raw data from networks with sensitive species and data restrictions are not included here but can be accessed by submitting a data request to the associated networks. See our DMA statement for more details. Here, we provide the raw tree-by-tree data from the fully open-access networks. This affects the first R script only. The grid-level output data, generated in script 1 and shared in the data folder, can be used to replicate the results of this study. --NOTE 2 | The bash script [2_IPM_4_HPC.sh] sets up an array to run the IPM script [2_IPM_4_HPC.R] in batches of 100 species on a high-performance computing cluster. We ran into an issue for two species [index=30 & 962] that had an immortal survival-growth matrix. To find this error, we duplicated the IPM R and bash scripts and modified the indexing. These files are in the folder named "Error Check". 3_IPM_postProc.R is then used to recompile the IPM outputs. --Data inputs required for these scripts and the intermediate output data that are both produced and later required by these scripts are stored in the folder named "data". -----
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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.128 | 0.044 |
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".