You are given a paper review either in Spanish or English language. Based on the given review, evaluate the paper by classifying it to one of the five classes: (1) very negative, (2) negative, (3) neutral, (4) positive, and (5) very positive. Note that URLs in the text have been replaced with [Link].

Let me give you an example: El articulo presenta un interesante desarrollo de un caso, como una propuesta mas de metodologia de inicio para la didactica de la logica de programacion. Las conclusiones presentadas no tienen mucho fundamento en terminos del logro del objetivo de inducir de esta manera el razonamiento logico.
The answer to this example can be: very positive
Here is why: Based on the review text, we can see that paper evaluation is very positive.

OK. solve this:
The paper describes an experience concerning the automated inspection of spectra for the Pipeline Hubble Legacy Archive Grism data. Comments: In the conclusions the authors say that "We have identified two classes of flawed spectra which were not picked up by the automatic classification because of their very small number of training samples. Per construction machine learning techniques can not classify such outliers." This sentence should be further explained. As Hastie, Tibshirani, and Friedman say in The Elements of Statistical Learning (see Chapter 7): "it is too difficult to give a general rule on how much training data is enough; among other things, this depends on the signal-to-noise ratio of the underlying function, and the complexity of the models being fit to the data. " So, by just saying that the number of training samples is small you do not provide enough information to decide whether machine learning techniques are adequate or not to solve a problem. Other minor comments: + If reference [2] has not yet been written or published it should be deleted from the paper. The dataset may be of scientific importance. It is mainly descriptive.
Answer:
positive