The Basin of Mexico Hydrogeological Database: Implementation, Queries and Interaction with Open Source Software
Bibliographic record
Abstract
Integrated Water Management at the Basin level concept was introduced in the 1990s, and is a goal in every national and local water management plan. Unfortunately this goal has not been achieved mainly due to a lack of both tools and data management, as data must be gathered from different sources, and converted from diverse formats into a consistent database. Compounding this problem is the fact that in some regions different water agencies are in charge of water supply as is the case in the Basin of Mexico, in which Mexico City and its Metropolitan Zone are located. The inhabitants of the Basin of Mexico, which comprises five different political entities and in which different agencies are in charge of water supply rely on the Basins aquifer system as its main water supply source. \n \n \n \nNo regional hydrogeological database in this area however exists therefore a Relational Database Management System was developed, and its use with a Geographic Information System is proposed in order to improve regional data management in the study area. Data stored in this new database (called the Basin of Mexico Hydrogeological Database) comprises data on climatological, borehole and runoff variables, readily providing information for the development of hydrogeological models. A simple example is used to show how geostatistical analysis can be done using the data directly from this database. The structure of the database supports easy maintenance and updating, representing a valuable tool for the development of regional studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".