Multituberculata

Multi tool use
Multi tool use




Multituberculata (nomen a Cope anno 1884 inventum) est mammalium ordo.



Systema taxinomicum |


  • Imperium: Eukaryota
    • Regnum: Animalia
      • Gradus: Metazoa
        • Subgradus: Eumetazoa
          • Subregnum: Bilateria
            • Infraregnum: Eucoelomata
              • Supraphylum: Deuterostomia
                • Infraphylum: Chordonia
                  • Phylum: Chordata
                    • Subphylum: Vertebrata
                      • Infraphylum: Gnathostomata
                        • Supraclassis: Tetrapoda
                          • Infraclassis: Amniota
                            • Ramus: Mammaliaformes
                              • Classis: Mammalia
                                • Infraclassis: Theriiformes
                                  • Infraclassis: † Allotheria
                                    • Ordo: † Multituberculata (Cope,1884)

                                      • Familia: † Albionbaataridae

                                      • Familia: † Arginbaataridae

                                      • Familia: † Bolodontidae

                                      • Familia: † Hahnodontidae

                                      • Familia: † Kogaionidae

                                      • Subordo: † Cimolodonta

                                      • Subordo: † Gondwanatheria





















Notae |



  • Hahn, G. & Hahn, R., 2003: New multituberculate teeth from the Early Cretaceous of Marocco. Acta Palaeontologica Polonica: Vol. 48.

  • Kielan-Jaworowska, Z., Cifelli, R. L. & Luo, Z.-X., 2004: Mammals from the Age of Dinosaurs – origins, evolution, and structure. Columbia University Press, New York, 2004.

  • McKenna, M. C. & Bell, S. K., (eds.) 1997: Classification of mammals – above the species level. Columbia University Press, New York, 1997.



Nexus externi |


  • Multituberculata apud Systema Natura





Wikispecies-logo.svg
 Vide Multituberculata apud Vicispecies.






Commons-logo.svg
Vicimedia Communia plura habent quae ad Multituberculata spectant.


.mw-parser-output .stipula{padding:3px;background:#F7F8FF;border:1px solid grey;margin:auto}.mw-parser-output .stipula td.cell1{background:transparent;color:white}




Mammalia
Haec stipula ad mammale spectat. Amplifica, si potes!



WF7ZiG6aEDi dKo0t1CQnA1ZZQ xXX57,XGvgu5FZRIPNyZ2I02wBx Tsy03iEE t15O,pjk,fTH
-
lwuLHcfcWArzt2PQxO5ait7KNykvMNWXVEsq

Popular posts from this blog

Tabula Rosettana

Image
Tabula multilinguis Rosettana in Museo Britannico ostenditur. Tabula Rosettana, [1] etiam titulo OGIS 90 agnita, est stela decreto de rebus sacris in Aegypto anno 196 a.C.n. lato inscripta. Tabula iuxta Rosettam Aegypti, urbem in delta Nili et ad oram maris Mediterranei iacentem, anno 1799 a milite Francico reperta est. Inventio stelae, linguis duabus scripturisque tribus inscriptae, eruditis Instituti Aegypti statim nuntiata est; ibi enim iussu imperatoris Napoleonis eruditi omnium scientiarum (sub aegide Commissionis Scientiarum et Artium) properaverant cum expeditione Francica. Qua a Britannis mox debellata, tabula Rosettana Londinium missa hodie apud Museum Britannicum iacet. Textus Graecus cito lectus interpretationi textuum Aegyptiorum (in formis hieroglyphica et demotica expressorum) gradatim adiuvit. Denique textum plene interpretatus est Ioannes Franciscus Champollion. Ab opere eruditorum cumulativo coepit hodiernus scripturae hieroglyphicae linguaeque Aegyptiae a...
Read more

How to label and detect the document text images

Image
1 $begingroup$ This is what I mean as document text image: I want to label the texts in image as separate blocks and my model should detect these labels as classes. NOTE: This is how the end result should be like: The labels like Block 1, Block 2, Block 3,.. should be Logo, Title, Date,.. Others, etc. Work done: First approach : I tried to implement this method via Object Detection, it didn't work. It didn't even detect any text. Second approach : Then I tried it using PixelLink. As this model is build for scene text detection, it detected each and every text in the image. But this method can detect multiple lines of text if the threshold values are increased. But I have no idea how do I add labels to the text blocks. PIXEL_CLS_WEIGHT_all_ones = 'PIXEL_CLS_WEIGHT_all_ones' PIXEL_C...
Read more

LSTM sequence prediction: 3d input to 2d output

Image
1 $begingroup$ I have this LSTM model model = Sequential() model.add(Masking(mask_value=0, input_shape=(timesteps, features))) model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2, return_sequences=False)) model.add(Dense(features, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) and shapes X_train (21, 11, 5), y_train (21, 5) . Each timestep is represented by 5 features and return_sequences is set to False because I want to predict one 5D array (the next timestep) for each input sequence of 11 timesteps. I get the error ValueError: y_true and y_pred have different number of output (5!=1) If I reshape the data as X_train (21, 11, 5), y_train (21, 1, 5) instead I get the error ValueError: Inva...
Read more