Economic importance, taxonomic representation and scientific priority as drivers of genome sequencing projects
Bibliographic record
Abstract
BACKGROUND: Of the approximately two hundred sequenced plant genomes, how many and which ones were sequenced motivated by strictly or largely scientific considerations, and how many by chiefly economic, in a wide sense, incentives? And how large a role does publication opportunity play? RESULTS: In an integration of multiple disparate databases and other sources of information, we collect and analyze data on the size (number of species) in the plant orders and families containing sequenced genomes, on the trade value of these species, and of all the same-family or same-order species, and on the publication priority within the family and order. These data are subjected to multiple regression and other statistical analyses. We find that despite the initial importance of model organisms, it is clearly economic considerations that outweigh others in the choice of genome to be sequenced. CONCLUSIONS: This has important implications for generalizations about plant genomes, since human choices of plants to harvest (and cultivate) will have incurred many biases with respect to phenotypic characteristics and hence of genomic properties, and recent genomic evolution will also have been affected by human agricultural practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".