Improvised Water Procurement in Wilderness Medicine: A Comparative Review of Yield, Energy Cost, and Field Suitability
Bibliographic record
Abstract
ABSTRACT Introduction Access to potable water remains a fundamental need in wilderness and disaster medicine. Improvised hydration techniques—such as solar stills, transpiration bags, and rain catchment—are widely taught but poorly compared. This review evaluates six low-resource water procurement methods for survival relevance. Methods A structured narrative review was conducted across PubMed, Google Scholar, and grey-literature sources from January 1990 to March 2025. Included studies reported water yield, labor time, disinfection potential, or energy cost. A modified Newcastle–Ottawa Scale assessed study quality (max = 8). Comparative energy-return-on-hydration (EROH) was calculated using MET estimates. Results Twenty-seven studies (17 field, 10 lab) from 10 countries were included (NOS median 5, IQR 4–6). Rain catchment yielded 5–10 L/event with minimal labor and no energy cost. Direct filtration produced >10 L/day when surface water was available. Boiling achieved complete pathogen removal but required fuel and time. Solar stills produced <0.6 L/day and demanded 375–500 kcal per build. Dew and transpiration methods yielded <1 L/day and required favorable humidity or sunlight. Solar stills were the least energy-efficient method. Table 1 summarizes comparative metrics; Figure 2 presents a decision-support algorithm. Conclusions Solar stills, though historically emphasized in survival curricula, offer poor yield and an unfavorable energy profile. Modern tools like filters, catchment tarps, and chemical disinfection should be prioritized in both practice and training. Wilderness educators and responders should re-align survival instruction with evidence-based hydration strategies.
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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 | 0.017 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".