Bibliographic record
Abstract
In Centre du Quebec, we established 395 circular plots (each 201.06 m2) in 42 forest patches across the region. Each forest had between 8 - 14 plots; in each plot, trees greater than 10 cm at 1.3 m (or diameter at breast height, DBH) were identified to species. We harmonized species names using The Plant List with the R package "Taxonstand" (Cayuela et al. 2019). In the dataset, we provide the following variables: forest patch id (patch_id), latitude and longitude of the centroid of each patch in degrees (patch_lat_dec, patch_long_dec), patch area in hectares (patch_area_ha), patch perimeter in meters (patch_perim_m), plot id (plot_id), species binomial (spp_binomial), species ID in TPL (TPL_ID), taxonomic status (taxon_status), and species abundance in terms of numbers of individuals (abundance). If you use this dataset, please cite this article as well: D. Craven, E. Filotas, V. A. Angers, C. Messier, Evaluating resilience of tree communities in fragmented landscapes: linking functional response diversity with landscape connectivity. Diversity and Distributions 22, 505–518 (2016).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.129 | 0.021 |
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".