Pemanfaatan Quantum GIS Cloud Untuk Pemetaan Polygon Area Kandang Peternakan di Wilayah Kabupaten Probolinggo
Bibliographic record
Abstract
Department of Husbandry and Animal Health (Disnakkeswan) Probolinggo, is a government agency that has data on livestock, one of which is the location mapping data for the stables. This data is needed by Disnakkeswan and the general public to find information on the location of the livestock sheds in the Probolinggo district. Currently, the data on the location of the cage which is owned by the agency is still in the form of excel table data. This creates difficulties for the Animal Husbandry and Animal Health Service (Disnakkeswan) and the general public when looking for information on the location of the livestock pen. Among other things, the difficulty made them have to waste time finding the location of the farm stables. Given the roles and responsibilities of the Department of Animal Husbandry and Animal Health (Disnakkeswan) Probolinggo Regency above, an application is needed that will support or facilitate this task. The information presented in the Geographical Information System mapping the location of livestock stalls is the location of the co-ordinates for beef cattle, dairy and ducks. which can provide information about the location (stables) of the farm in the form of a map using a geographic information system with QGis Cloud.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.015 |
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".