Bibliographic record
Abstract
We selected 395,289 coordinates of eucalypt distribution points in GBIF from 2001 to 2021. After removing erroneous coordinate data (such as those located on the sea or in the central Australian desert), we retained 313,146 eucalypt coordinate data from GBIF. In total, we used 313,236 location data points (313,146 from GBIF, 44 from CNKI, and 46 from Web of Science) of eucalypt in this study. We input the 313,236 eucalypt IFP distribution points into Google Earth Engine (GEE) 47. Subsequently, we extracted the NPP data for each point from the “MODIS/Terra Net Primary Production Gap-Filled Yearly L4 Global 500” dataset in GEE. Lastly, we intergrated the following data into our dataset: 1) 21 climatic factors, including annual mean vapor pressure deficit (Annual mean VPD) and vapor pressure deficit of warmest quarter (warmest quarter VPD); 2) 15 soil factors; and 3) groundwater percentile.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.035 |
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".