Evaluation of Distributed Sensor Configurations for Rainwater Harvesting Monitoring Systems
Bibliographic record
Abstract
Abstract Increasing risk of water scarcity in Mexico City has led to expansion of the practice of rainwater harvesting (RWH). However, it remains difficult to assess the benefits of the increased number of installations due to uncertainty in new users’ propsensity towards long-term adoption of these systems. This research proposes the use of sensors to aid in the study of RWH system adoption. A challenge that has presented itself in the instrumentation of RWH systems is the cost-effective collection of data from multiple sources that are physically separate at a common ‘gateway’ node for transmission. This study presents and evaluates three potential approaches to solving this problem: (1) the use of wires to connect sensors to the gateway node, (2) the use of LoRa-enabled wireless nodes configured in a self-organizing mesh network, and (3) LoRa-enabled nodes configured in a star network topology. An analysis of the cost of each configuration was performed, indicating that the wired system ($330.9 USD) was notably cheaper than the wireless options (> $425.3 USD) due to the expense of multiple LoRa radios. However, the potential cost saving advantage of LoRa mesh nodes sharing a network between households allows for the possibility of a per-household deployment cost even cheaper than the wired system. A qualitative analysis also explores the substantial limitations of the wired system due to challenges in its installation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".