Monitoring clearcutting and subsequent rapid recovery in Mediterranean coppice forests with Landsat time series
Bibliographic record
Abstract
This dataset considers information from clear-cut coppice forest occurred between 1999 and 2015 in three different area of Tuscany Region (Italy). The dataset consists in one shape file and two csv files. The shape file contains in 2371 spatial polygons of clear-cut occurred in the three area between 1999 and 2015. The polygons were generated using visual interpretation of the Landsat Time Series and high resolution regional ortomosaic. Photo interpreters delineated the spatial extent of each clear-cut and recorded the year of harvest, adopting a minimum mapping unit of 0.1 hectares. The database associated with shape file report information related with area extent of clear-cut, year of clear-cut and the forest types that is associated with the clear-cut derived by the Tucant Regional Forest Inventory (RFI) data that give information on dominant species. The .csv files reported the temporal characteristic of clear-cut based on Landsat Time Series data and based on Airborne Laser Scanner (ALS) Canopy Height Model (CHM), in order to describe the temporal trend of clear-cut based on remote sensing data.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".