Finding solutions from space: Essential Biodiversity Variables (RS-EBVs) for conservation planning
Bibliographic record
Abstract
The world is experiencing a biodiversity crisis. Recent studies have shown that important changes in biodiversity (unprecedented shifts in the species composition of ecological assemblages) (Dornelas at al 2014; Magurran et al 2015; McGill et al 2015) and biodiversity loss (Newbold et al 2015) together threaten the world's ecosystems and the services they provide. Biodiversity change is linked to the transformations of natural habitats, invasive species and climate change. In face to such wide changes, new international initiatives such as IPBES (http://www.ipbes.net/), Future Earth (http://www.futureearth.org/) seek to build global capacity to promote the sustainable use and conservation of biodiversity. However, despite these efforts, global biodiversity, and the ecosystem functions it supports, is increasingly threatened by anthropogenic impacts. Yet it is still difficult to assess progress towards the Aichi Biodiversity Targets for 2011-2020 set by the Convention on Biological Diversity (CBD). To focus priorities, ecologists have proposed classes of 'essential biodiversity variables' EBV's, including species traits and populations, and ecosystem function and structure. But measuring these on the ground is laborious and limited. The open access availability of satellite images from new sensors characterized by various spatial and temporal resolutions provides new challenges and possibilities for biodiversity conservation. Key parameter derived from Remote sensing data (RS) could be used to develop a set of the EBV's indicators. The joint use of remote sensing data sources with various spatial, temporal and spectral resolutions is essential for accessing the different descriptors of natural habitats. Issues of scalability remain challenging with a view towards upscaling to a global level. Still RS-EBV's present some limitations but they constitute a promising basis to support policy-making in order to fulfil the conservation strategy.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".