UWW 261
topics from UWW 261
topics from UWW 261
Fichier Détails
| Cartes-fiches | 16 |
|---|---|
| Langue | English |
| Catégorie | Biologie |
| Niveau | Université |
| Crée / Actualisé | 18.09.2014 / 03.04.2023 |
| Lien de web |
https://card2brain.ch/box/uww_261
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types of knowledge
personal knowledge
group knowledge
scienfitic knowledge
system, target, transformation knowledge
personal knowledge
genetic
by experience
by learning
group knowledge
by experience
by learning
transmitted between persons
not written, just told from generation to generation
(modern form = internet)
system, targed and transformation knowledge
system: how works the system
targed: what is the targed, what do people want
transformation: how to reach this targed
--> all three can be get scientific, but just system k. has to be
goals of scientific research
idealistiic: curiosity, knowledge, understanding
Practical:
- increase wealth
- maintain health
- personal career
history of knowledge
550BC - 1950AD
greek, age of physics
1950-
age of biology
life science
ecology, environmental science
drivers of knowledge gain
miliary
economic wealth
health
curiosity
information storage
genes and brains
drawings and written text
electronic media
fossils, samples, specimen, artifacts
rationalism
pure logic
independent of observation
deductive: from general to specific
Platon, Descartes
in mathematics and physics
+ consisten theories, independent of perception
- often not realistic
empiricism
by experience
observaions, experiments
inductive: from specific to general
Aristotle, Locke, Hume
biology and env. sciences
+ related to natural phenomena, real, objective
- perception bias, needs many replicates
examples of scientific revolutions
Earth flat, Sun circles around Earth ––> Earth spherical, circles around Sun
All species existed for ever or were created at same time ––> continuous evolution of new species
r- and K-selection
r: high rmax, low K
K: low rma, high K
trade off between r and K
Levins about theories/models
trade-off between generality, realism, precision of predictions
often to much weight on precision
more general predictions can be sufficient
criteria for a good model (6)
-consistent with observations
- consistent with theory
- testable
- parsimony (= simplicity: better fewer than more parameters)
- explanatory power, how much variation can be explained, R^2
- heuristic value
What would you prefer, a logarithmic or a 2nd degree polynomial
curve for a survival curve
logarithmic
more parsimonius
explain growth better
What would you prefer, a logarithmic or a 2nd degree polynomial
curve for a biomass–density relationship
polynomial,
it is just y=b2*x^2
contant total yield
explain phenomena better
