LandSin: A differential ML and google API-enabled web server for real-time land insights and beyond
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
LandSin , a web application with a back-end database, is developed for global land value estimation by combining polynomial regression and differential privacy models. Leveraging local amenities and property details, LandSin offers key features, e.g., accurate land value and price predictions, affordability and habitability analysis, and terrain insights using Google Maps. In addition, it facilitates useful infographics, helping stakeholders identify economically deprived but habitable areas for balanced regional development. It also supports real estate agencies and community planners in finding habitable land by making data-driven decisions regarding land investments and regional planning, ensuring informed and strategic choices. • LandSin offers dynamic land value prediction using machine learning and differential privacy. • It integrates Google Maps for terrain insights and habitability analysis for real estate decisions. • LandSin helps real estate agencies assess investment opportunities by analyzing market trends. • Differential privacy ensures user data security while providing accurate land value estimates. • Governments can use LandSin for regional planning by identifying economically deprived areas.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it