Germination and growth data for jack pine in different ground cover types (lichens and feather mosses)
Bibliographic record
Abstract
This dataset is associated with the article by Marine Pacé, Nicole J. Fenton, David Paré, Franck O.P. Stefani, Hugues B. Massicotte, Linda E. Tackaberry and Yves Bergeron entitled "Lichens contribute to open woodland stability in the boreal forest through detrimental effects on pine growth and root ectomycorrhizal development". It includes data of black spruce germination (greenhouse), 0-6 month-old seedling growth (greenhouse) and 2-3 year-old seedling growth (greenhouse and field, 49°22’N, 79°13’W). Seeds, seedlings and saplings were subjected to different treatments: 3 types of ground cover (bare soil, feather mosses, lichens), different levels of fertilization, and different levels of shade above the moss layer. The degree of seedling root mycorrhization, as well as the different types of ectomycorrhizal associations (genotypes, OTUs) were analyzed. The dataset also contains information on the water content of the greenhouse pots, the chemical composition (N, P, K, Ca, Mg, Na, cation base) of the organic layer in the field, as well as the chemical composition of the first centimeters of mineral soil below the moss and lichen layer (data for greenhouse and field experiments).
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.023 |
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".