Global humid tropics forest structural condition and forest structural integrity maps
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Remotely sensed maps of global forest extent are widely used for conservation assessment and planning. Yet, there is increasing recognition that these efforts must now include elements of forest quality for biodiversity and ecosystem services. Such data are not yet available globally. Here we introduce two data products, the Forest Structural Condition Index (SCI) and the Forest Structural Integrity Index (FSII), to meet this need for the humid tropics. The SCI integrates canopy height, tree cover, and time since disturbance to distinguish short, open-canopy, or recently deforested stands from tall, closed-canopy, older stands typical of primary forest. The SCI was validated against estimates of foliage height diversity derived from airborne lidar. The FSII overlays a global index of human pressure on SCI to identify structurally complex forests with low human pressure, likely the most valuable for maintaining biodiversity and ecosystem services. These products represent an important step in maturation from conservation focus on forest extent to forest stands that should be considered "best of the last" in international policy settings.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it