Wireless Sensing Networks for Environmental Monitoring: Two Case Studies from Tropical Forests
Bibliographic record
Abstract
The emergence of new environmental monitoring tools utilizing Wireless Sensor Networks (WSNs) represents new opportunities to understand locally driven ecological processes in tropical environments at larger spatial and temporal scales, while at the same time opens new opportunities for eScience research. In this paper we present results from the outcome of two WSNs deployments aimed to evaluate this new technology in tropical environments. Leaf temperature and Photosynthetically Active Radiation (PAR) were measured at high temporal resolution in Panama and Brazil, respectively. Our results indicate that WSN technologies can be used effectively enough to measure important micro-meteorological variables that are sensitive to climate change and land use/cover change. Some of the temperatures recorded during our experiments in Panama were significantly high than those suggested as critical environmental thresholds for tropical environments. Our PAR results from Brazil showcase the value of this technology to evaluate the role of light patterns on ecosystem succession as a result of regional land use/cover change process. Our experiments indicate the WSN can serve as a key element in future understandings of ecosystem process driven by eScience in the years to come.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".