SNDT WOMEN'S UNIVERSITY
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Estimating geomagnetic field detection sensitivity of pigeons and passerine migrants using deep machine learning (Record no. 131775)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01752nam a2200133 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250624b |||||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Vedachalam Narayanaswamy |
| 245 ## - TITLE STATEMENT | |
| Title | Estimating geomagnetic field detection sensitivity of pigeons and passerine migrants using deep machine learning |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | P 388-395 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. biblio.abstract | The capability of homing pigeons and passerine migrants to derive navigation-rated information from the<br/>geomagnetic field (GMF), enabling them to navigate<br/>from unfamiliar sites, is a subject of research. Despite<br/>the vast trajectory information available from field experiments, the true accuracy of their map and compass<br/>sense are seldom reported. The recent developments in<br/>bird geo-tagging, precision world magnetic model and<br/>deep machine-learning capabilities enable us to understand the mechanisms underlying their innate abilities<br/>in true navigation. In this article, we machine-learnt<br/>the GMF anomaly in a 10 km2 region and analysed the<br/>flight path efficiency for a 10-km GMF-anomaly-guided<br/>trajectory using the developed deep-learning-based artificial intelligence (AI) algorithm. From the simulated<br/>flight path and comparing the computed efficiencies<br/>with the field-reported results, it is observed that the<br/>sensitivity of the GMF gradient detection sensory system<br/>in pigeons and passerine migrants in familiar and unfamiliar regions are in the range of 1–3 nT and 0.5–2.5 nT<br/>respectively. Identified results shall help implement AIbased solutions for understanding spatiotemporal bird<br/>migration and enacting environmental conservation<br/>policies. |
| 654 ## - SUBJECT ADDED ENTRY--FACETED TOPICAL TERMS | |
| Subject | <a href="Deep learning">Deep learning</a> |
| -- | <a href="machine learning">machine learning</a> |
| -- | <a href="magnetic field">magnetic field</a> |
| -- | <a href="navigation">navigation</a> |
| 773 0# - HOST ITEM ENTRY | |
| Host Biblionumber | 125299 |
| Host Itemnumber | 112521 |
| Title | Current Science |
| International Standard Serial Number | 0011-3891 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Journal Article |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Location (home branch) | Sublocation or collection (holding branch) | Date acquired | Koha issues (times borrowed) | Piece designation (barcode) | Koha date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | SNDT Juhu | SNDT Juhu | 24/06/2025 | JP669.4 | 24/06/2025 | 24/06/2025 | Journal Article |