<scp>EcoregionsTreeFinder</scp>—A Global Dataset Documenting the Abundance of Observations of > 45,000 Tree Species in 828 Terrestrial Ecoregions
Bibliographic record
Abstract
ABSTRACT Motivation As recently defined in the Resource Manual for Target 2 of the Kunming‐Montreal Global Biodiversity Framework, the primary outcome of “ecological restoration” is the conservation and restoration of biodiversity. The “golden rules of tree planting” reflected in The Global Biodiversity Standard advocate maximising native tree species. The Ecoregions2017Resolve global map was developed to enhance systematic planning for terrestrial biodiversity conservation. EcoregionsTreeFinder is a unique new database that provides lists of native tree species for the ecoregions of the Ecoregions 2017 map. Besides being directly applicable to aid the planning or evaluation of restoration projects within the framework of the Ecoregions 2017 map, EcoregionsTreeFinder can be used in other applications, such as biogeographical investigations or to guide completion efforts of tree presence observations across ecoregions. Main Types of Variable Contained Tree species records for 828 terrestrial ecoregions, showing the number of presence observations for 48,129 tree species. The 453,053 ecoregion‐species records include information on the number of observations in different bioclimatic zones within each ecoregion for zones defined by the Climatic Moisture Index, average monthly temperatures > 10°C, and the Maximum Climatological Water Deficit. Also included with these records is information on the expected native distribution of species across ecoregions, allowing filtering of native tree species from a selected ecoregion. Spatial Location and Grain Global, 6–3,922,506 km2 (ecoregions), 30 arc‐seconds (bioclimatic zones within ecoregions). Time Period and Grain 1946–2021, presence observations filtered from the Global Biodiversity Information Facility. Major Taxa and Level of Measurement 48,129 tree species, number of observations within ecoregions and within bioclimatic zones nested within ecoregions. Software Format Three data sets (.txt) and 18 atlas sheets showing the distribution of bioclimatic zones within ecoregions (.pdf).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.024 |
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".