SITE SUITABILITY ANALYSIS OF SALT FARMS IN PANGASINAN USING GEOSPATIAL DATA AND ANALYTIC HIERARCHY PROCESS
Bibliographic record
Abstract
Abstract. The Philippines has imported 93% (550,000 MT) of its annual salt requirement in 2020. Such heavy reliance on salt importation may be attributed to the two decades of neglect and steady downward production trend of the local salt industry. There is a need to increase local salt production because Filipinos consume salt on a daily basis for over 14,000 uses, ranging from being a household commodity to being utilized for agricultural and industrial processes. It is therefore imperative to increase the number of areas allocated for local salt production. In this study, a suitability analysis for solar salt farm operations in Pangasinan was conducted using geospatial data and Analytic Hierarchy Process (AHP). Physical land variables (slope, land cover, distance from coastline, distance from river, and soil texture) and meteorological factors (temperature, humidity, rainfall, wind speed) were considered to assess the suitability of an area for salt production. Results showed that meteorological factors (62.5%) outweigh the physical land variables (37.5%) by a 25% margin, suggesting the heavy influence of climate on salt production. 4629.88 km2 (or 86.6% of the total land area of Pangasinan) was identified to be suitable for salt production with 496.01 km2 (9.5%) being moderately suitable and 3800.87 km2 (77.1%) being rated as low. The moderately suitable areas were validated using existing salt farm locations in two sites, Dasol and Alaminos City, and showed 62.13% and 28.06% overlapping areas, respectively. The main explanation for these relatively low percentage figures is the gaps in the output suitability map due to the slope and soil datasets. The concentration of the moderately suitable areas is situated along coastal municipalities of Pangasinan including Bolinao, Dasol, Alaminos City, Sual, Agno, Labrador, Infanta, San Fabian, Anda, and Bani. The findings of this study can help policymakers, relevant government agencies and stakeholders in the planning process of revitalizing the salt industry by expanding existing salt farms or constructing new ones in Pangasinan.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".