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AN EXPERIMENTAL TEST OF LOCAL ADAPTATION IN SOIL BACTERIA

2003· article· en· W2037954090 on OpenAlexaff
DOROTHÉ BELOTTE, JEAN BAPTISTE CURIEN, R. Craig MacLean, Graham Bell

Bibliographic record

VenueEvolution · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsEdaphicBiologyLocal adaptationAdaptation (eye)EcologySoil testTest siteSoil waterDemography

Abstract

fetched live from OpenAlex

We extracted bacterial isolates of similar colony morphology from spatially located soil samples within 1 ha of old-growth forest. The same soil samples were used to prepare growth medium. Each isolate was then cultured in each medium and its growth recorded. There was no overall tendency for isolates to grow more successfully in their home site (i.e., the medium derived from the soil sample from which they had been extracted). Most isolates grew very poorly, however, and when the analysis was restricted to the minority of vigorous isolates there was clear evidence of local adaptation: isolates tended to grow better at their home site than did isolates from elsewhere and grew better at their home site than they did at other sites. The variation of growth within the 1-ha plot made up a complex fitness landscape of peaks, ridges, and valleys. Most of the vigorous isolates were found at or near a local fitness (growth) peak, although seldom at a global peak. In consequence, there was a tendency for growth to diminish away from the home site. The home isolate was about 50% more fit than average at its home site; fitness diminished exponentially away from the home site at a rate of 0.0577 per meter. These figures are similar to those previously reported for plants. This selection gradient has matched the bacterial assemblage to the edaphic structure of the environment, although the fit is far from perfect.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.216
Teacher spread0.182 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations105
Published2003
Admission routes1
Has abstractyes

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