Development of a large area biodiversity monitoring system driven by remote sensing
Bibliographic record
Abstract
Biodiversity is a multifaceted concept that often eludes simple operational definitions. As a result, a variety of definitions have been proposed each with varying levels of complexity and scope. While different definitions of biodiversity exist, the basic unit of measurement for the vast majority of studies is conducted at the species level. Traditional approaches to measuring species richness provide useful, yet spatially constrained information. Remote sensing offers the opportunity for large area characterizations of biodiversity in a systematic, repeatable, and spatially exhaustive manner. Based on this review we examine the potential for a national biodiversity monitoring system for Canada driven by remote sensing, a country approaching 1 billion ha in area, with the aim of producing recommendations that are transferable for regional or continental applications. A combination of direct and indirect approaches is proposed, with four selected key indicators of diversity that can be derived from Earth observation data: productivity, disturbance, topography, and land cover. Monitoring these indicators through time at an ecosystem level has the potential to provide a national early warning system, indicating where areas of potential biodiversity change may be occurring. We believe the large area biodiversity monitoring system as outlined would provide an initial stratification of key areas where regional and local scale analysis can be focused, while also providing context-specific information for species collection data.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".