Sensitivity of commercial scanners to microchips of various frequencies implanted in dogs and cats
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
OBJECTIVE: To evaluate the sensitivity of 4 commercially available microchip scanners used to detect or read encrypted and unencrypted 125-, 128-, and 134.2-kHz microchips under field conditions following implantation in dogs and cats at 6 animal shelters. DESIGN: Cross-sectional study. Animals-3,949 dogs and cats at 6 animal shelters. PROCEDURES: Each shelter was asked to enroll 657 to 660 animals and to implant microchips in 438 to 440 animals (each shelter used a different microchip brand). Animals were then scanned with 3 or 4 commercial scanners to determine whether microchips could be detected. Scanner sensitivity was calculated as the percentage of animals with a microchip in which the microchip was detected. RESULTS: None of the scanners examined had 100% sensitivity for any of the microchip brands. In addition, there were clear differences among scanners in regard to sensitivity. The 3 universal scanners capable of reading or detecting 128- and 134.2-kHz microchips all had sensitivities > or = 94.8% for microchips of these frequencies. Three of the 4 scanners had sensitivities > or = 88.2% for 125-kHz microchips, but sensitivity of one of the universal scanners for microchips of this frequency was lower (66.4% to 75.0%). CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated that some currently available universal scanners have high sensitivity to microchips of the frequencies commonly used in the United States, although none of the scanners had 100% sensitivity. To maximize microchip detection, proper scanning technique should be used and animals should be scanned more than once. Microchipping should remain a component of a more comprehensive pet identification program.
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 | 0.002 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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