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Record W7085088046 · doi:10.5281/zenodo.17290458

Retrofits and Revisions: How Evolutionary Theory Fails the Test of Predictive Science

2025· article· en· W7085088046 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languageen
FieldComputer Science
TopicVideo Surveillance and Tracking Methods
Canadian institutionsSt. Clair College
Fundersnot available
KeywordsGeneticistDarwinismEvolutionary theoryPopulationCore (optical fiber)Mechanism (biology)Mutation rateMutation

Abstract

fetched live from OpenAlex

Science on Trial: Evolution vs. Creation The gold standard of science is prediction. If a theory can’t make testable, risky forecasts—and if it consistently fails when put to the test—then it doesn’t deserve the title of science. Evolution has been presented as biology’s untouchable framework… but history reveals a long trail of failed predictions, retroactive just-so stories, and an inability to generate genuinely novel structures through its core mechanism of mutation and selection. Meanwhile, the Young Earth Creationist (YEC) model—dismissed by many as unscientific—has quietly built a track record of making specific, accurate predictions:✅ Genetic similarity across “distant” organisms✅ Hierarchical clustering of kinds✅ Mutation rates that match a recent origin✅ Limits to beneficial mutations✅ Functional “junk DNA” (ERVs, regulatory networks, Hox clusters, etc.)✅ Rapid speciation and founder effects✅ Unity of the human race And the results? Time and again, data aligns with YEC predictions—while evolutionary theory scrambles to retrofit explanations after the fact. In this study, we put the predictions of both models head-to-head across genetics, development, and population biology. The verdict is striking: creation’s design-based framework repeatedly outperforms evolution’s story-driven narrative. From “junk DNA” turning out functional, to mutation rates exposing hard limits, to the stubborn absence of macroevolutionary innovation—modern evidence is shouting what many scientists won’t admit: the Darwinian framework is crumbling. Even insiders acknowledge it: “For what good is a theory that is guaranteed to agree with all conceivable observations? …Is that not exactly the situation with Darwinism?” — Richard Lewontin, Harvard Geneticist “Nearly all the evolutionary stories I learned as a student… have now been debunked.” — Derek Ager, Imperial College, London 📖 As Dr. Richard Bliss once put it:“The miracles required to make evolution feasible are far greater in number and far harder to believe than the miracle of creation.” ⚡ It’s time to put predictions on trial.⚡ It’s time to ask hard questions about falsifiability.⚡ It’s time to reconsider which model really explains the data. Dive in and see why the evidence doesn’t just challenge evolution—it points to creation as the better scientific model.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.002
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 head0.024
GPT teacher head0.270
Teacher spread0.246 · 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.

Study designOther design
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

Citations0
Published2025
Admission routes1
Has abstractyes

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