Data Paper. Data Paper
Bibliographic record
Abstract
File List Files are Ascii text, tab delimited. "-999" denotes a lack of information for that field (sampling was not carried out in that particular plot for that year). Croche_VegCover.txt-- 7660 records. Croche_SeedDens.txt -- 1447 records. Croche_Env.txt -- 557 records. Croche_BA.txt -- 205 records. Description The Lac Croche data set covers a nine-year period (1998–2006) of detailed understory vegetation sampling of a temperate North American forest located in the Station de Biologie des Laurentides (SBL), Québec, Canada. After having been submitted to logging in the late 19th and early 20th centuries followed by a major fire in 1923, the forest is currently in a transition state dominated by pioneer canopy tree species. The sampling design is based on the annual re-sampling of 43 permanent 400-m2 plots along five transects running parallel to an elevation gradient from a lake (Lac Croche) to the top of a hill. Abundances of all understory vascular plants (tree seedlings, herbs, and shrubs) are included in the data set and are expressed either as absolute densities or cover classes, depending on life form. The location and elevation of each plot, as well as some key environmenatl descriptors such as slope, rockiness, canopy openness, age of the largest tree, basal area of mature trees, and a number of soil variables are also available. The Lac Croche data set should prove useful for testing hypotheses about forest vegetation dynamics at different sacles, as well as to test new statistical tools developed for the analysis of the spatio-temporal variation of plant distributions. Sampling is ongoing, and new data will be added every year. Key words: community patterns ; forest succession; spatial variation; Station de Biologie des Laurentides (SBL); temperate forest; temporal variation; understory plants; vegetation dynamics .
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.653 | 0.573 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".