OpenLLM-France/wikimedia huggingface dataset:
# Adventist Adventurer Awards and Answers/Spotter

* Spotter
* Busy Bee

## Spot, identify, and collect a picture of the following
### four different animals

* Siamese Cat
* Yellow Labrador Dog

### four different types of motor vehicles

* Boeing 737 Airplane
* 2011 Nissan Leaf Car
* School Bus
* Train

### two different types of airplanes

* A police department Bell 206 helicopter
* Commercial airplane Airbus A300-600

### four different nature items

* Apple
* Lilly
* Pine Tree
* Sunflower
* Lake Tahoe on the border of California and Nevada
* Moon

### four different birds

* Blue Bird
* Blue Jay
* Crow
* Parakeet
* Robin

## Observe three of the following people in uniform

* Police Officer
* Fireman
* Nurse
* Postman
* Paramedics
* Traffic Warden

## Find, read, and explain what the text Matthew 28:20 is telling us.

* Matthew 28:20 (NIV)
* and teaching them to obey everything I have commanded you. And surely I am with you always, to the very end of the age.

## Observe your church. Is there anything you can do to keep it clean? Write or draw what you can do to help.

* Don't leave trash behind after you
* If you see trash pick it up and put it in the bin
* Organize the Hymnal books
* Ask the church board if you can help take care of the garden

## Remember 10 or 15 items after looking at them for two minutes.

Encourage a “good spotter” by placing familiar items on a desk or table. After two minutes, cover items and have children tell you or write down items they remember. Play this game several times, teaching the children to be more observant.

## External Resources

vteAdventist Adventurer Awards :
# Adventist Adventurer Awards and Answers/Sign Language

* Sign Language
* Helping Hand

## Learn how to send the receive words, using the manual alphabet.

In this site you will find three secret messages to decode Thinkquest

## Learn at least 50 words.
### ME

    With a 1-hand, point at the middle of your chest, making contact once.

### YOU, HIM, HER

    Point towards the person's chest with a 1-hand.

### ASL

    Fingerspell ASL

### AMERICAN SIGN LANGUAGEAMERICAN SIGN LANGUAGE

    With intertwined 5-hands with palms vertical and at right angles (in the wall plane), complete one or two circles with your hands together.
    With 1-hands, draw circles in the wall plane perpendicular to your shoulders. This version fills your signing space. Alternatively, spin your forefingers around each other in a smaller motion. Either one is in the center of your signing space.
    With L-hands, contact the thumbs in the centre of your signing space with palms facing down, then wiggle them outwards simultaneously.

### BOY

    As if grasping your ball cap in a flattened C-hand twice. Open and close your hand twice with the palm facing across your forehead.

### GIRL

    With a A-hand, your thumb traces your jawbone from near your ear to your chin.

### MOTHER

    With a 5-hand, tap your thumb to the outside of your chin twice.

### FATHER

    With a 5-hand, tap your thumb to the outside of your forehead.

### DON'T KNOW

    With a palm-hand, touch the side of your forehead with the fingertips, then turn the hand out and away from your face.

### YES

    With a S-hand, "nod" your hand up and down a few times in front of your chest, facing the viewer.

### NO

    Using a "pinch"-hand, open and close twice with your palm facing the viewer.
    For a more forceful version, open and close your fingers only once, faster with a hand motion forwards towards the subject of your command.

### DEAF

    With the 1-hand, touch your chin at the ear, then at the front of your chin. This version is used for culturally-deaf individuals.
    An alternative sign, used for CLINICALLY DEAF (meaning literally EAR CLOSED) uses a 1-hand to point to your ear, then two palm-hands to signify closing a door. Bring the hands together like two shutters in front of that ear.

### NAME

    Using two H-hands, tap the second joint of the middle fingers together on top of one another.

### SORRY

    With a loose A-hand, rub the centre of your chest and look sorry.

### PLEASE

    With a loose palm-hand, rub the centre of your chest.

### FINGERSPELL

    Starting with a 5-hand, mime fingerspelling by fluttering your fingers in alternation while moving your hand about the fingerspelling part of your signing space.

## Learn and present at least one simple Christian song.

Jesus Loves Me in Sign Language - You Tube Video

## Sign a simple Bible verse.

Go to You Tube to learn John 3:16 in sign language

## External Resources

You Tube John 3:16
You Tube Jesus Love Me
vteAdventist Adventurer Awards :
# Chronicles of Narnia/Books/The Horse and His Boy/Chapter 4

← Chapter 3 | Chapter 5 →

## Overview

Title: Shasta Falls In with the Narnians
# Chronicles of Narnia/Books/The Horse and His Boy/Chapter 5

← Chapter 4 | Chapter 6 →

## Overview

Title: Prince Corin
# Chronicles of Narnia/Books/The Horse and His Boy/Chapter 8

← Chapter 7 | Chapter 9 →

## Overview

Title: In the House of the Tisroc
# Chronicles of Narnia/Books/The Lion, the Witch, and the Wardrobe

| The Horse and His Boy →
← The Magician's Nephew | The Horse and His Boy →
For general information on this book, please see the Wikipedia article The Lion, the Witch, and the Wardrobe.

## Chapters

    Chapter 1: Lucy looks into a Wardrobe
    Chapter 2: What Lucy found there
    Chapter 3: Edmund and the Wardrobe
    Chapter 4: Turkish Delight
    Chapter 5: Back on This Side of the Door
    Chapter 6: Into the Forest
    Chapter 7: A Day with the Beavers
    Chapter 8: What happened after Dinner
    Chapter 9: In the Witch's House
    Chapter 10: The Spell begins to break
    Chapter 11: Aslan is nearer
    Chapter 12: Peter's First Battle
    Chapter 13: Deep Magic from the Dawn of Time
    Chapter 14: The Triumph of the Witch
    Chapter 15: Deeper Magic from before the Dawn of Time
    Chapter 16: What happened about the Statues
    Chapter 17: The Hunting of the White Stag

## Overview

The first book written in the Narnia series, and often recommended as the proper starting place in the series, The Lion, the Witch, and the Wardrobe is written from the point of view of the four Pevensie children, Lucy, Edmund, Peter, and Susan. While its prime audience is children aged 8 to about 11, the story has enough depth to make it a satisfying, if short, read for even adult readers, and the characters show realistic development over the course of the book.

## Book Highlights

* New places visited: Lantern Waste, the Stone Table, Cair Paravel, Beaver's home, Mr. Tumnus' home, White Witch's castle
* Title refers to: Aslan, the White Witch, and the portal between our land and Narnia
# Chronicles of Narnia/Books/The Horse and His Boy/Chapter 6

← Chapter 5 | Chapter 7 →

## Overview

Title: Shasta Among the Tombs
# Chronicles of Narnia/Books/The Horse and His Boy/Chapter 7

← Chapter 6 | Chapter 8 →

## Overview

Title: Aravis in Tashbaan
# Chronicles of Narnia/Books/The Horse and His Boy/Chapter 9

← Chapter 8 | Chapter 10 →

## Overview

Title: Across the Desert
# Chronicles of Narnia/Books/The Lion, the Witch, and the Wardrobe/Chapter 1

| Chapter 2 →

## Overview

Title: Lucy looks into a Wardrobe

## Synopsis

The four Pevensie children, Susan, Peter, Edmund, and Lucy, have been sent to the Professor's house in the country to escape the bombing in London. Finding that it is quite the big old house, and that there is a lot of countryside to explore, they are prepared for adventure, but instead are confined to the house by rain. Only slightly daunted, they explore the house, finding, among other things, one room containing only a single wardrobe. As the others move on to the next room, Lucy, curious, opens the door and, entranced by the fur on the coats hanging in the wardrobe, walks in. Finding the wardrobe far deeper than she expected, she keeps walking, and shortly discovers that she is walking on snow, in the middle of a forest, with a single lamp-post burning in the gloom.
Wondering at finding a forest in the back of a wardrobe, and a London streetlamp in the middle of that forest, Lucy is further bemused to hear approaching footsteps. And very shortly, the footsteps are found to be those of a Faun, with an armload or parcels just as if he was on his way home from shopping. Seeing Lucy, the faun is so surprised that he drops all his parcels.
# Chronicles of Narnia/Books/The Last Battle

← The Magician's Nephew |
← The Silver Chair |
For general information on this book, please see the Wikipedia article The Last Battle.

## Chapters

    Chapter 1: By Caldron Pool
    Chapter 2: The Rashness of the King
    Chapter 3: The Ape in its Glory
    Chapter 4: What Happened that Night
    Chapter 5: How Help came to the King
    Chapter 6: A Good Night's Work
    Chapter 7: Mainly About Dwarfs
    Chapter 8: What News the Eagle Brought
    Chapter 9: The Great Meeting on Stable Hill
    Chapter 10: Who will go into the Stable?
    Chapter 11: The Pace Quickens
    Chapter 12: Through the Stable Door
    Chapter 13: How the Dwarfs Refused to be Taken In
    Chapter 14: Night falls on Narnia
    Chapter 15: Farther Up and Farther In
    Chapter 16: Farewell to Shadowlands

## Overview

The seventh and final book published in the Narnia series, this book is largely the events and battles that immediately precede the final destruction of the land of Narnia. While its prime audience is children aged 8 to about 11, the story has enough depth to make it a satisfying, if short, read for even adult readers, and the characters show realistic development over the course of the book.

## Book Highlights

* New places visited: None.
* Title refers to: The end of Narnia
# Chronicles of Narnia/Books/The Lion, the Witch, and the Wardrobe/Chapter 10

Title: The Spell begins to break
# Canadian Refugee Procedure/Schedules to the RPD Rules
## RPD Rules Schedule 1 - Claimant’s Information and Basis of Claim

The text of this schedule to the rules follows:
# Canadian Tort Law

This text is intended to provide an overview of the current law of torts, and its relevant jurisprudence and history.

## Contents

* Introduction
* Trespass to person
* Trespass to property
* Negligence
* Strict liability
* Defamation
* Nuisance
* Extra-contractual obligation (Quebec)
* Appendix I - case summaries
# Animal Behavior/Maturation
## Maturation

Individuals, during their progression from infancy to adulthood, experience a variety of changes, including physical, cognitive, behavioral, and psychosocial.

### Puberty

Successfully navigating the transition from juvenile, to meet the demands of adulthood, remains one of the trickiest tasks any individual will likely face. The changes in behavioral repertoire that occur during puberty are reflected in the maturation of endocrine, neural and neurochemical systems. The influence of rising gonadal hormones on male and female reproductive behaviors are well understood. However, corresponding changes also exist in structure and function of neural circuits underlying a much broader range of behavioral and cognitive development.
# Sicilian Defense: Kveinis Variation

This variation has its pros and cons but is generally considered to be inferior to most variations. The point of Qb6 is to put pressure on white's B2 pawn and D4 knight. However, this plan does not work because white can sacrifice the B2 pawn with Be3, then play Nd2 and attack the queen if black captures. If white just plays Nc3 or another solid move, then black can just develop and be fine. However, this opening has some pros. It is very easy to learn because the plans are simple and the theory is low. This can work at the beginner level and in blitz or bullet games.
# Mengarini Variation

This opening is an interesting variation of the Wing Gambit. The idea is to prepare b4 with a3. The main continuation is 2...Nc6 although many moves are possible.

## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation. .
1.e4 c5 2.a3
| 2 |
| ... Nc6 | = |
| ... d5 | = |
| ... e6 | = |
| ... g6 | = |
| ... d6 | = |
| ... e5 | +/= |
When contributing to this Wikibook, please follow the Conventions for organization.

## References

* Challenging the Sicilian with 2.a3!?. 2004. Alexei Bezgodov. ISBN 954-8782-375.
# Chess Opening Theory/1. e4/1...c5/2. Nf3/2...e6/3. d4/3...cxd4/4. Nxd4/4...Nf6/5. Nc3/5...a6

a6 prevents Nb5.
When contributing to this Wikibook, please follow the Conventions for organization.
# Mengarini Variation

The most common and logical follow-up to 2. a3 in this situation is 3. b4, a delayed Wing Gambit, with the idea to deflect the c5-pawn from the center.

## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation. .
1. e4 c5 2. a3 Nc6
| 3 | 4 | 5 | 6 |
| b4 Nf6 | b5 Nd4 | c3 Ne6 | e5 Nd5 | = |
When contributing to this Wikibook, please follow the Conventions for organization.
# Kan Sicilian

Moves:1.e4 c5 2.Nf3 e6 3.d4 cxd4 4.Nxd4 a6 5.Nc3
    5...Qc7: Sicilian Paulsen; Scheveningen
    5...Nc6: Sicilian Taimanov
    5...d6: Sicilian Kan - Knight Variation
    5...b5: Sicilian Kan Modern
When contributing to this Wikibook, please follow the Conventions for organization.

## References

* Kasparov, Garry, & Keene, Raymond 1989 Batsford chess openings 2. ISBN 0-8050-3409-9.
# Kan Sicilian

Moves:1.e4 c5 2.Nf3 e6 3.d4 cxd4 4.Nxd4 a6
    5. Bd3
    5. Nc3
    5. c4: Sicilian, Kan, Maroczy bind (Reti variation)

## Theory table

1.e4 c5 2.Nf3 e6 3.d4 cxd4 4.Nxd4 a6
| 5 | 6 | 7 |
| Bd3 Nf6 | O-O Qc7 | Qe2 d6 | += |
| Nc3 Qc7 | Bd3 Nf6 | O-O Nc6 | = |
| c4 Nf6 | Nc3 Bb4 | Qd3 Qc7 | = |
When contributing to this Wikibook, please follow the Conventions for organization.

## References

* Kasparov, Garry, & Keene, Raymond 1989 Batsford chess openings 2. ISBN 0-8050-3409-9.
* This Chess related article is a stub. Please assist Wikibooks by expanding it.
# Snyder Variation

This opening is an interesting variation of the Sicilian. The idea is to fianchetto the dark-squared bishop on b2. The main continuation is 2...Nc6 although many moves are possible.

## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation. .
1.e4 c5 2.b3
| 2 |
| ... Nc6 | = |
| ... d6 | = |
| ... b6 | = |
| ... Nf6 | = |
| ... e6 | = |
| ... g6 | +/= |
When contributing to this Wikibook, please follow the Conventions for organization.

## References

* Sabotaging the Sicilian, French & Caro-Kann with 2.b3. 2018. Marek Soszynski.
# Sicilian 2...e6
## Theory table

1.e4 c5 2.Nf3 e6 3.d4 cxd4 4.Nxd4 Nf6 5. Nc3
| | 5 | 6 | 7 |
| | Nc3 d6 | Be2 Nc6 | O-O Be7 | = |
| Taimanov | ... Nc6 | Ndb5 Bb4 | a3 Bxc3+ | += |
| Pin Variation | ... Bb4 | = |
| Koch Variation | ... Bb4 | e5 Nd5?! | Bd2 0-0 | Nxd5 Bxd2+ | Qxd2 exd5 | += |
When contributing to this Wikibook, please follow the Conventions for organization.

## References

* This Chess related article is a stub. Please assist Wikibooks by expanding it.
# Data Mining Algorithms In R/Packages/RWeka/Weka classifier trees
## Description

R interfaces to Weka regression and classification tree learners.

## Usage

J48(formula, data, subset, na.action, control = Weka_control, options = NULL)
LMT(formula, data, subset, na.action, control = Weka_control, options = NULL)
M5P(formula, data, subset, na.action, control = Weka_control, options = NULL)
DecisionStump(formula, data, subset, na.action, control = Weka_control, options = NULL)

## Arguments

formula, a symbolic description of the model to be fit.
data, an optional data frame containing the variables in the model.
subset, an optional vector specifying a subset of observations to be used in the fitting process.
na.action, a function which indicates what should happen when the data contain NAs.
control, an object of class Weka_control giving options to be passed to the Weka learner.
options, a named list of further options, or NULL (default).

## Details

There are a predict method for predicting from the fitted models, and a summary method based on evaluate_Weka_classifier. There is also a plot method for fitted binary Weka_trees via the facilities provided by package party. This converts the Weka_tree to a BinaryTree and then simply calls the plot method of this class (see plot.BinaryTree) with slight modifications to the default arguments. Provided the Weka classification tree learner implements the “Drawable” interface (i.e., provides a graph method), write_to_dot can be used to create a DOT representation of the tree for visualization via Graphviz or the Rgraphviz package.
J48 generates unpruned or pruned C4.5 decision trees (Quinlan, 1993).
LMT implements “Logistic Model Trees” (Landwehr, 2003; Landwehr et al., 2005).
M5P (where the ‘P’ stands for ‘prime’) generates M5 model trees using the M5’ algorithm, which was introduced in Wang & Witten (1997) and enhances the original M5 algorithm by Quinlan(1992).
DecisionStump implements decision stumps (trees with a single split only), which are frequently used as base learners for meta learners such as Boosting.
The model formulae should only use the ‘+’ and ‘-’ operators to indicate the variables to be included or not used, respectively. Argument options allows further customization. Currently, options model and instances (or partial matches for these) are used: if set to TRUE, the model frame or the corresponding Weka instances, respectively, are included in the fitted model object, possibly speeding up subsequent computations on the object. By default, neither is included.

## Value

A list inheriting from classes Weka_tree and Weka_classifiers with components including:
classifier, a reference (of class jobjRef) to a Java object obtained by applying the Weka
buildClassifier, method to build the specified model using the given control options.
predictions, a numeric vector or factor with the model predictions for the training instances (the results of calling the Weka classifyInstance method for the built classifier and each instance).
call, the matched call.
# Data Mining Algorithms In R/Packages/RWeka/Weka clusterers
## Description

R interfaces to Weka clustering algorithms.

## Usage

Cobweb(x, control = NULL)
FarthestFirst(x, control = NULL)
SimpleKMeans(x, control = NULL)
XMeans(x, control = NULL)
DBScan(x, control = NULL)

## Arguments

x, an R object with the data to be clustered.
control, an object of class Weka_control, or a character vector of control options, or NULL (default).

## Details

There is a predict method for predicting class ids or memberships from the fitted clusterers.
Cobweb implements the Cobweb (Fisher, 1987) and Classit (Gennari et al., 1989) clustering algorithms.
FarthestFirst provides the “farthest first traversal algorithm” by Hochbaum and Shmoys, which works as a fast simple approximate clusterer modeled after simple k-means.
SimpleKMeans provides clustering with the k-means algorithm.
XMeans provides k-means extended by an “Improve-Structure part” and automatically determines the number of clusters.
DBScan provides the “density-based clustering algorithm” by Ester, Kriegel, Sander, and Xu. Note that noise points are assigned to NA.

## Value

A list inheriting from class Weka_clusterers with components including:
clusterer, a reference (of class jobjRef) to a Java object obtained by applying the Weka buildClusterer method to the training instances using the given control options.
class_ids, a vector of integers indicating the class to which each training instance is allocated (the results of calling theWeka clusterInstance method for the built clusterer and each instance).
# Data Mining Algorithms In R/Packages/RWeka/Weka control
## Description

Set control options for Weka learners.

## Usage

Weka_control(...)

## Arguments

Arguments of control options.

## Details

The available options for a Weka learner, foo say, can be queried by WOW(foo) and then conveniently set by Weka_control.

## Value

A list of class Weka_control which can be coerced to character for passing it to Weka.
# Data Mining Algorithms In R/Packages/RWeka/Weka classifier rules
## Description

R interfaces to Weka rule learners.

## Usage

JRip(formula, data, subset, na.action, control = Weka_control, options = NULL)
M5Rules(formula, data, subset, na.action, control = Weka_control, options = NULL)
OneR(formula, data, subset, na.action, control = Weka_control, options = NULL)
PART(formula, data, subset, na.action, control = Weka_control, options = NULL)

## Arguments

formula, a symbolic description of the model to be fit.
data, an optional data frame containing the variables in the model.
subset, an optional vector specifying a subset of observations to be used in the fitting process.
na.action, a function which indicates what should happen when the data contain NAs.
control, an object of class Weka_control giving options to be passed to the Weka learner.
options, a named list of further options, or NULL (default).

## Details

There are a predict method for predicting from the fitted models, and a summary method based on evaluate_Weka_classifier.
JRip implements a propositional rule learner, “Repeated Incremental Pruning to Produce Error Reduction” (RIPPER), as proposed by Cohen (1995).
M5Rules generates a decision list for regression problems using separate-and-conquer. In each iteration it builds an model tree using M5 and makes the “best” leaf into a rule.
OneR builds a simple 1-R classifier, see Holte (1993).
PART generates PART decision lists using the approach of Frank and Witten (1998).
The model formulae should only use the ‘+’ and ‘-’ operators to indicate the variables to be included or not used, respectively. Argument options allows further customization. Currently, options model and instances (or partial matches for these) are used: if set to TRUE, the model frame or the corresponding Weka instances, respectively, are included in the fitted model object, possibly speeding up subsequent computations on the object.

## Value

A list inheriting from classes Weka_rules and Weka_classifiers with components including:
classifier, a reference (of class jobjRef) to a Java object obtained by applying the Weka buildClassifier method to build the specified model using the given control options.
predictions, a numeric vector or factor with the model predictions for the training instances (the results of calling the Weka classifyInstance method for the built classifier and each instance).
call, the matched call.