Five Radiometric Landscape of Kerguelen Islands
Bibliographic record
Abstract
Landscape mapping is crucial for monitoring and understanding a wide range of ecological and social processes, as well as for addressing major issues such as land use planning, biodiversity assessment, health-environment interactions and monitoring the impacts of environmental and resource management policies. In order to make the landscape approach operational, it is necessary to have robust methodological frameworks and specific tools for characterising and mapping landscapes at different scales. In this context, our objective is to develop a map of the major landscapes of the Kerguelen archipelago using only satellite time series (MODIS SITS). This work is based on the use of the MOD13Q1 product (NDVI, 250 m) covering the period 2003–2022, which is used to identify and characterise the main landscape structures. The dataset provided includes: a methodological diagram describing the steps of the approach adopted; all the data used, structured around four Essential Landscape Variables (ELVs) derived from the NDVI time series; the final map of the five major types of radiometric landscapes; This approach illustrates the potential of satellite time series for mapping landscapes in sub-Antarctic environments and has resulted in the first radiometric landscape map of the archipelago. It thus provides a valuable reference base for future environmental analyses, such as the study of landscape trajectories, monitoring vegetation dynamics, and assessing the impacts of climate change. This work was supported by the Centre National d’Études Spatiales (CNES) through the TOSCA EVOLKER project. Louise Lemettais PhD grant was funded both by university of Lille and CNES.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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 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".