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Specific Solutions to General Proble...

Saberski, Erik.

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Specific Solutions to General Problems in Data Science and Ecology.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Specific Solutions to General Problems in Data Science and Ecology./
Author:	Saberski, Erik.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	95 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
Contained By:	Dissertations Abstracts International85-10B.
Subject:	Biological oceanography. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30988993
ISBN:	9798382262437

Specific Solutions to General Problems in Data Science and Ecology.
Saberski, Erik.

Specific Solutions to General Problems in Data Science and Ecology. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 95 p.

Source: Dissertations Abstracts International, Volume: 85-10, Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2024.

Nature is hard to predict. Rules and relationships you discover about a system today may be totally different tomorrow. These relationships do not change randomly over time; rather, they change as the state of the system evolves. In a deterministic view of the world, similar states lead to similar outcomes. In this thesis, I leverage this principle to better understand and ultimately predict, complex systems.In chapter 1, I work closely with the National Parks Service to understand variables that influence flow target values through the Everglades National Park. The Tamiami Trail Flow Formula, a linear (not state-dependent) was previously developed to predict such values. In this chapter I show that with only minor adjustments to their linear approach, a non-linear (state-dependent) predictor can be made with significant prediction improvement.Chapter 2 focuses on the role of scale in understand ecosystem relationships. Using both models and real world examples I show that not one scale can capture all of the dynamics of a real world system: for example, some relationships are better resolved at an annual timescale while others are best resolved monthly.In chapter 3 I develop a new method for classifying systems based on the delay in their dynamic relationships. This method is applied to study the behavioral states of the nematode Caenorhabditis elegans. By analyzing the causal relationships between eigenvectors that represent the worm's posture ("eigenworms"), I am able to classify the behavioral state of the worm (foraging or reacting to a harmful stimulus). Additionally, I demonstrate that this technique can identify genetic mutations in these worms solely through analysis of their bodily movements.This work demonstrates the that powerful models and non-linear relationships can be extrapolated directly from data without the need for assumptions or fixed equations.

ISBN: 9798382262437Subjects--Topical Terms:

2122748
Biological oceanography.
Subjects--Index Terms:

Causal relationships

Specific Solutions to General Problems in Data Science and Ecology.
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245 1 0 $a Specific Solutions to General Problems in Data Science and Ecology.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 95 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
500 $a Advisor: Sugihara, George.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2024.
520 $a Nature is hard to predict. Rules and relationships you discover about a system today may be totally different tomorrow. These relationships do not change randomly over time; rather, they change as the state of the system evolves. In a deterministic view of the world, similar states lead to similar outcomes. In this thesis, I leverage this principle to better understand and ultimately predict, complex systems.In chapter 1, I work closely with the National Parks Service to understand variables that influence flow target values through the Everglades National Park. The Tamiami Trail Flow Formula, a linear (not state-dependent) was previously developed to predict such values. In this chapter I show that with only minor adjustments to their linear approach, a non-linear (state-dependent) predictor can be made with significant prediction improvement.Chapter 2 focuses on the role of scale in understand ecosystem relationships. Using both models and real world examples I show that not one scale can capture all of the dynamics of a real world system: for example, some relationships are better resolved at an annual timescale while others are best resolved monthly.In chapter 3 I develop a new method for classifying systems based on the delay in their dynamic relationships. This method is applied to study the behavioral states of the nematode Caenorhabditis elegans. By analyzing the causal relationships between eigenvectors that represent the worm's posture ("eigenworms"), I am able to classify the behavioral state of the worm (foraging or reacting to a harmful stimulus). Additionally, I demonstrate that this technique can identify genetic mutations in these worms solely through analysis of their bodily movements.This work demonstrates the that powerful models and non-linear relationships can be extrapolated directly from data without the need for assumptions or fixed equations.
590 $a School code: 0033.
650 4 $a Biological oceanography. $3 2122748
650 4 $a Ecology. $3 516476
650 4 $a Natural resource management. $3 589570
653 $a Causal relationships
653 $a Data science
653 $a Dynamic modeling
653 $a Empirical dynamic modeling
653 $a Prediction
690 $a 0416
690 $a 0329
690 $a 0528
710 2 $a University of California, San Diego. $b Scripps Institution of Oceanography. $3 3543449
773 0 $t Dissertations Abstracts International $g 85-10B.
790 $a 0033
791 $a Ph.D.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30988993