data - Leveraging biomass procurement to mitigate carbon emissions at the stand level: a case study in eastern Canadian forests
Bibliographic record
Abstract
This repository provides input and output datasets used in “Leveraging biomass procurement to mitigate carbon emissions at the stand level: a case study in eastern Canadian forests”.Input datasets include data collected in 2019 from pre-harvest mature forests, following the experimental design described in Canuel et al. (2024), "Post-harvest regeneration is driven by ecological factors rather than wood procurement intensity in eastern Canadian forests" (doi:10.1093/forestry/cpae008). The experimental design included six experimental sites, each with a randomized block design using four blocks. Two to four ground sampling plots per block were established, for a total of 84 sampling plots. Data were collected following ground sampling guidelines, version 5.0 published in 2008, from the Canada's National Forest Inventory (see https://nfi.nfis.org/resources/groundplot/Gp_guidelines_v5.0.pdf).Only pre-harvest stumps data (pre-harvest_stumps.csv) are provided in this repository. Stumps were tallied and measured within circular sampling plots (r = 3.99 m). Only data from sampling plots where one or more stumps were measured are shown. Other empirical data from the pre-harvest stand inventory used in the study are available at the following repository: https://doi.org/10.6084/m9.figshare.22587139.v1.We provided output datasets from carbon modelling for the six experimental sites and seven scenarios in five separate files ("[...]_scenario.csv"). These output datasets support the results of our study.We also provide a summary file that describes the rows and columns in the datasets of this repository.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.018 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".