Bibliographic record
Abstract
At the next World Congress, Vancouver 2014, complete genomes will be as popular as SNP microarrays were at the Leipzig venue. Simply consider that the human 1000 genomes main paper has just been submitted and that the pace of improvement in sequencing has not reached a plateau yet. There is no discussion on whether this is sensible in all situations. It will just happen. Therefore, it is worth noting that the main tools breeders will need to fully benefit from the sequence data were present in the plenary genomics talks, even if sometimes disguised – I am referring to mixed models, systems biology and coalescence for Peter Visscher’s, Trudy Mackay’s and Kristian Andersen’s talks, respectively. A synthetic theory that combines all three might have as much impact in biology as the unified theory had in particle physics during the last century. Visscher’s contribution was centred in the discrepancy between estimated pedigree heritability and the amount of variance explained by loci identified in genome-wide association studies. Perhaps not unsurprisingly, power and SNP ascertainment may explain the difference between pedigree and SNP-based results. Mackay presented ongoing work where several inbred drosophila lines have been sequenced to completion and phenotyped for dozens of remarkable traits, ranging from longevity to many behavioural components. Despite the muscle shown, it is a pity that the analyses seemed incomplete, but we should soon see important papers coming out of the effort. The lecture by Andersen reminded me at times of a school lesson to uninitiated disbelievers. Nevertheless, it bore important points of which a typical animal breeder is usually unaware. The main one is that selection bends the black box: there is no perfect crime and the culprit leaves a footprint in the pattern of nucleotide variability. The problem, mentioned in the talk, is that the murderer can be quite intelligent and – as in CSI – hide the action among multiple false clues and escape. The rest of the meeting was spent placidly, either you chose to stay in a single room and arrived soon enough, or multivariately/hectily if you made transitions between rooms. I prefer to call them transversions. Overall, on the non-selection genomics side, I would have expected more results from large-scale genotyping studies and more variety of papers dealing with population genetics topics. A bit too soon? Lack of WCGALP appeal? If the latter were true, it would be a real pity because genes do exist and do matter, even if they do not always need to be identified. I would like to finish by citing the paper by Hill et al. (2008, Plos Genet4: e1000008). It had nine citations at the time of writing so let us make it a ten now.
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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.069 | 0.027 |
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".