The Smart Forest Conundrum: Contextualizing Pitfalls of Sensors and AI in Conservation Science for Tropical Forests
Bibliographic record
Abstract
The term ‘smart forest’ is not yet common, but the proliferation of sensors, algorithms, and technocentric thinking in conservation, as in most other aspects of our lives, suggests we are at the brink of this evolution. While there has been some critical discussion about the value of using smart technology in conservation, a holistic discussion about the broader technological, social, and economic interactions involved with using big data, sensors, artificial intelligence, and global corporations is largely missing. Here, we explore the pitfalls that are useful to consider as forests are gradually converted to technological sites of data production for optimized biodiversity conservation and are consequently incorporated in the digital economy. We consider who are the enablers of the technologically enhanced forests and how the gradual operationalization of smart forests will impact the traditional stakeholders of conservation. We also look at the implications of carpeting forests with sensors and the type of questions that will be encouraged. To contextualize our arguments, we provide examples from our work in Kibale National Park, Uganda which hosts the one of the longest continuously running research field station in Africa.
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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 | 0.039 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.016 | 0.103 |
| Scholarly communication | 0.018 | 0.035 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".