Details

Vsebnost težkih kovin v "zemlji za lončnice" : diplomsko delo
ID Štefanec, Duša (Author), ID Zupančič, Nina (Mentor) More about this mentor... This link opens in a new window, ID Grčman, Helena (Comentor)

.pdfPDF - Presentation file, Download (2,36 MB)
MD5: 443D5B65BE2797A419DA3D4185DBE4E6
PID: 20.500.12556/rul/71403bdc-f3e3-4d0f-b313-9f3ae13faabe

Language:Slovenian
Keywords:geokemija, pedologija, umetna tla za lončnice, potencialno strupene prvine, humificirana organska snov, dodatek šote
Work type:Undergraduate thesis
Typology:2.11 - Undergraduate Thesis
Organization:NTF - Faculty of Natural Sciences and Engineering
Place of publishing:Ljubljana
Publisher:[D. Štefanec]
Year:2016
Number of pages:VII, 42 f.
PID:20.500.12556/RUL-87899 This link opens in a new window
UDC:55
COBISS.SI-ID:1283934 This link opens in a new window
Publication date in RUL:19.12.2016
Views:3590
Downloads:483
Metadata:XML DC-XML DC-RDF
:
ŠTEFANEC, Duša, 2016, Vsebnost težkih kovin v “zemlji za lončnice” : diplomsko delo [online]. Bachelor’s thesis. Ljubljana : D. Štefanec. [Accessed 11 April 2025]. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=87899
Copy citation
Share:Bookmark and Share

Secondary language

Language:English
Title:Heavy metal content in "potting soil"

Similar documents

Similar works from RUL:
  1. DEEP LEARNING METHODS FOR BIOMETRIC RECOGNITION BASED ON EYE INFORMATION
  2. Part of speech tagging of slovene language using deep neural networks
  3. Automatic classification of buildings with deep learning
  4. Object detection and classification in aquatic environment using convolutional neural networks
  5. Superposition and compression of deep neutral networks
Similar works from other Slovenian collections:
  1. Time series classification based on convolutional neural networks
  2. The preparation of photos' dataset and its classification using deep neural networks
  3. Prediction of geospatial raster data using convolutional neural networks
  4. Development of an advanced system for lane detection on GPU platforms
  5. Comparison of different deep neural network learning algorithms in autonomous driving

Back