Epilogue: Toward a Global Biodiversity Monitoring System
Bibliographic record
Abstract
Abstract Monitoring the ongoing declines in biodiversity and ecosystem health is essential for formulating societal responses. The Group on Earth Observations Biodiversity Observation Network (GEO BON) was created in 2008 to develop a global monitoring network, but that is a huge task with much remaining work. Fortunately, satellite remote sensing can help by providing periodic, global data that is not otherwise available. Continued advances – many of which are discussed in this book’s 19 chapters – in science as well as sensor and computational technology have an increasingly important role. However, taking advantage of these requires bringing together a variety of elements and disciplines, and it is with this in mind that a conceptual system architecture is here suggested. Key elements of this cloud-based architecture include: explicit support for collaboration, particularly across disciplines, to facilitate development of new algorithms; access to a wide variety of data, including satellite, airborne, and in situ; recognition of the importance of integrating across sensors and scales; and access to significant CPU cycles for generating heavy products such as time series. Such a system would build upon GEO BON and contribute to its mission by being the core of its RS activities; like GEO BON itself, it would be a coordinated effort among many international players.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".