Towards a macroscope: Leveraging technology to transform the breadth, scale and resolution of macroecological data
Bibliographic record
Abstract
Abstract The problem Earth‐based observations of the biosphere are spatially biased in ways that can limit our ability to detect macroecological patterns and changes in biodiversity. To resolve this problem, we need to supplement the ad hoc data currently collected with planned biodiversity monitoring, in order to approximate global stratified random sampling of the planet. We call this all‐encompassing observational system ‘the macroscope’. The solution With a focus on the marine realm, we identify seven main biosphere observation tools that compose the macroscope: satellites, drones, camera traps, passive acoustic samplers, biologgers, environmental DNA and human observations. By deploying a nested array of these tools that fills current gaps in monitoring, we can achieve a macroscope fit for purpose and turn these existing powerful tools into more than the sum of their parts. An appeal Building a macroscope requires commitment from many fields, together with coordinated actions to attract the level of funding required for such a venture. We call on macroecologists to become advocates for the macroscope and to engage with existing global observation networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".