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Record W1999756930 · doi:10.5558/tfc77239-2

Black poplar: A model for gene resource conservation in forest ecosystems

2001· article· en· W1999756930 on OpenAlexvenueno aff
François Lefèvre, Davorin Kajba, Berthold Heinze, Peter Rotach, S.M.G. de Vries, J. Turok

Bibliographic record

VenueThe Forestry Chronicle · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsnot available
Fundersnot available
KeywordsRiparian zoneRiparian forestThreatened speciesForest managementHabitatAgroforestryForest ecologyEnvironmental resource managementIntact forest landscapeEcologyGeographyEcosystemBiodiversityEcosystem managementResource (disambiguation)Environmental scienceBiology

Abstract

fetched live from OpenAlex

Conservation of genetic resources of forest trees has become a major objective for the management of forests. Much theoretical work has been devoted to the subject, and implementation has already started at the local, national, or international scales. Poplars are probably the most representative and threatened forest tree species of old natural floodplain forests in the temperate zone. Gene conservation needs to be integrated with intensive breeding activities, habitat conservation and restoration. For Populus nigra, while research in genetics and ecology is reinforced, a combined conservation strategy is applied at the European scale; simultaneously, the conservation of riparian ecosystems is also a priority. Research and application benefit from each other. The question now is the evaluation of such an integrated strategy. Criteria and indicators for the follow-up of gene resource management are progressively developed, but still need to be tested on the operational scale. Key words: Populus nigra, poplar, gene resources, in situ conservation, ex situconservation, riparian ecosystem, sustainable management

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.034
GPT teacher head0.226
Teacher spread0.192 · 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 designSimulation or modeling
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

Citations26
Published2001
Admission routes1
Has abstractyes

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