Consumption Pattern Analysis of Water Consumption For Commercial Connections in Davao City
Bibliographic record
Abstract
This study applied the fundamental time series analysis to evaluate the distribution, trend, and later forecast of water consumption for commercial connections by classification of Davao City Water District (DCWD). As such, this study examined the relationship between time and the water consumption of Davao City’s commercial connections over time. The monthly water consumption of commercial connections, categorized by classification per water source systems, was the data source for this study, which ran from January 2015 to December 2019. The four categories of commercial connections’ time series statistics suggested that, from 24 million cubic meters in 2018, commercial consumption dropped to 20 million cubic meters in 2019. This decline is explained by a decrease in consumption of commercials 2.0 and 1.25 classification. The data showed that during the first quarter of the year, increases were observed within five years (short-term). For commercial type 2.0, in 2022, forecasted water consumption would reach 14 million cubic meters in some improved areas of operations. Hence, specific forecasting by type of connection requires in-depth analysis. Thus, further study is required for long-term forecasting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".