SURVEI TITER ANTIBODI RABIES PADA ANJING YANG MASUK KE \nINDONESIA MELALUI BANDARA SOEKARNO HATTA DALAM PERIODE OKTOBER 2018-JANUARI 2019
Bibliographic record
Abstract
The purpose of this study was to identify whether the level of protection of dogs \nentering Indonesia through Soekarno-Hatta Airport in the period October 2018 - \nJanuary 2019 meets the OIE’s protective antibody standard requirements, that is 0.5 \nIU / ml, as well as to find out the number of dogs with a level of protectivity based \non gender, age, country status, and dog breeds. The study was conducted by using \na secondary data of dogs imported through Soekarno-Hatta Airport, Jakarta in the \nperiod of October 2018 – January 2019. The result shows that there are 175 out of \n302 dogs that entered Indonesia through Soekarno-Hatta Airport in the period from \nOctober 2018 to January 2019, with antibody titers that did not comply with OIE \nstandards (not protective). Furthermore, based on gender, age, and country status, \nthere are 94, 117, 94 dogs with antibody titers that did not comply with OIE \nstandards (not protective) respectively, and breeds of dogs that have a tendency for \nantibody titers that are not protective are large and medium sized dogs (Labrador, \nRotweillers, and Alaskan Malamute).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".