Montanhas Adirondack

Multi tool use

As
montanhas Adirondack, no condado de Essex
As montanhas Adirondack são uma cordilheira do estado norte-americano de Nova Iorque que ultrapassa os 1200 metros de altitude em 40 dos seus picos, culminando nos montes Marcy (1628 m), Mclntyre (1557 m), Skylight (1500 m), Haystack (1498 m) e Dix (1475 m). São formados por rochas cristalinas e ricos em minérios de ferro (magnetites); contêm também granito, mármore, titânio e talco.
Formam a linha que divide as águas do rio São Lourenço e do Hudson. São abundantes os lagos, distinguindo-se entre eles o lago Champlain (193 km de comprimento, por 24 km de largura), lago George, lago Little Tupper, lago Raquette, lago Fulton Chain e outros. É uma região turística muito famosa, sobretudo para o turismo esportivo (esquiagem).
 |
Este artigo sobre geografia dos Estados Unidos é um esboço. Você pode ajudar a Wikipédia expandindo-o.
|
Referências |
- Nova Enciclopédia Portuguesa, Ed. Publicações Ediclube, 1996.
Controle de autoridade |
: Q357546
- WorldCat
- VIAF: 235541996
- EBID: ID
- GEC: 0000740
- GND: 4203429-2
- NARA: 10045462
- GeoNames: 5106772
|
OlaM6f
Popular posts from this blog
0
I found a lot of questions abount appendices and ToC. Many users want appendices to be grouped in an Appendix part, however some problems arise with ToC, hyperref, PDF viewer bookmarks, and so on. There are different solutions which require extra packages, command patching and other extra code, however none of them satisfies me. I almost found an easy way to accomplish a good result, where appendices are added to bookmarks in the right way and hyperref links point to the right page. However, the number of the "Appendix" part page is wrong (it's the number of appendix A). Is there any EASY way to fix that? This is a MWE: documentclass{book} usepackage[nottoc,notlot,notlof]{tocbibind} usepackage{hyperref} begin{document} frontmatter tableofcontents mainmatter part{First} chapter{...
1
In the sklearn.model_selection.cross_val_predict page it is stated: Generate cross-validated estimates for each input data point. It is not appropriate to pass these predictions into an evaluation metric. Can someone explain what does it mean? If this gives estimate of Y (y prediction) for every Y (true Y), why can't I calculate metrics such as RMSE or coefficient of determination using these results?
python scikit-learn cross-validation
share | improve this question
edited Nov 28 '18 at 17:52
desertnaut
20.3k 7 43 79
...
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ height:90px;width:728px;box-sizing:border-box;
}
0
I have written a function using curl to generate the token. I check whether the token exists; if not, then I execute the function, otherwise I skip this function and proceed to next. But I am not sure that it will work if a token is expired. Is there any command to identify the expired token and generates the new one by calling this function? #!/bin/ksh export V_TOKEN="gen_token_${V_DATE}.txt" #### Calling function to generate the token function callPOST { curl -X POST -H 'Content-Type: application/x-www-form-url' -d 'grant_type=password&username=usr01&password=pwd...