North American tree provenance trial and relevant historical climate data for seven species
Bibliographic record
Abstract
This dataset contains two databases: (1) test_sites_database and (2) trial_results_database. The test_sites_database contains location and climate information for tree provenance trial test sites. The climate information is summarized for the active (planting, measurement) years of the site. The trial_results_database contains the trial results (such as tree height, survival, etc.) for different seed sources associated the tree provenance test sites described in the test_sites_database. It also contains long-term climate summaries for the seed source origin locations. We obtained the trial results from a systemic review of publicly-available scientific studies and appended climate data to facilitate data analysis. Our purpose in providing these datasets is to ensure that the results of a variety tree provenance studies are available for use by researchers and forestry professionals, given how time-consuming and expensive these trials are to establish. When using the data, please also cite the original study. This can be found in the table "Guide to Data Sources", found in the folder "data source information" in both databases. The databases both contain the data in .csv and .txt format.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.034 |
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".