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Record W4406829733 · doi:10.1080/02698595.2025.2455681

Failures of Scale Separation in Biology and the Problem of Inter-Level Causation

2025· article· en· W4406829733 on OpenAlexafffund
Tudor M. Baetu

Bibliographic record

VenueInternational Studies in the Philosophy of Science · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrigins and Evolution of Life
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCausationSeparation (statistics)Scale (ratio)Statistical physicsMathematicsEpistemologyPhilosophyPhysicsStatisticsQuantum mechanics

Abstract

fetched live from OpenAlex

A conflict between evidence for causation and the metaphysical requirement of spatiotemporal distinct causal relata arises if the results of experiments commonly described as ‘bottom-up’ and ‘top-down’ are taken to demonstrate ontological determination dependencies between parts and wholes or their respective behaviours. It has been argued that the problem can be circumvented if experimental results are interpreted in terms of relationships between variables measured and manipulated at separating scales. I argue that scale separation fails in the case of biological phenomena due to the small number of molecules and molecular events underlying these phenomena, which makes it impossible to completely disentangle micro and macro-scale behaviours. I defend instead a causal mediation interpretation according to which experiments assess the effects of interventions targeting the composition and exposure of biological systems. Under this interpretation, causal relata are ontologically distinct fractions or parts of biological systems, thus avoiding the oddity of part-whole causation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.391
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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