Bibliographic record
Abstract
Progress occurs when people share ideas. Animal breeders apply statistics and mathematics to genetic and phenotypic data. All researchers and organizations should be encouraged to apply math and statistics to their data rather than be restricted by patents. Suppose that advances in data recording, computing, or genetic technology make a new area of animal breeding research attractive. Researcher A immediately begins collecting data to develop a new service, realizing that the data analysis methods can be derived and programmed easily after sufficient data are collected. Researcher B does not collect data or provide service but instead derives an approximate method for data analysis that could be useful if data were available. Researcher C derives an exact method to make more accurate predictions but only applies it to a very small data set. Suppose that researchers B and C both try to patent their math, and that both patents are granted. Researcher A finishes collecting data, derives similar data analysis methods, and begins offering a prediction service. Patent attorneys for researchers B and C then each sue researcher A for half of the profit, giving researcher A little incentive to either collect real data or provide real service. Patents cause real rather than imaginary problems. An example is the statistical analysis of test day data. Genetic evaluations in Australia had used similar methods for several years before the method was patented by a researcher in the United States. The patent failed in Europe because the method was obvious and lacked an inventive step. Canadian evaluations introduced a more advanced, direct evaluation of test day data but had to pay for a license even though the two-step method in the patent was not used. US evaluations use methods very similar to those patented, but were derived by selection index instead of best linear unbiased prediction (BLUP), thereby avoiding a license fee. However, negotiations on licensing and further US research have stalled because the patent is not viewed as valid and because we prefer providing genetic rankings and research to the public for free rather than for a fee. Genomic selection is the newest application of statistics and mathematics to genetic and phenotypic data. Published methods to analyse human DNA should not later be patented by others when applied to animal DNA. Many millions of dollars have been invested already to sequence genomes, develop single nucleotide polymorphism (SNP) chips, and genotype reference populations to enable genomic selection. Patent offices should allow researcher A to use this investment in genomic technology and not let researchers B and C put limits on the use of genomic technology. Applied and basic research both deserve funding, but patents may simply stop progress by others. Already SNP chips have been limited to include only random markers to avoid legal disputes that would result if patented genes were included. Governments grant patents to promote research in new areas, but patents on mathematical and statistical ideas may have the reverse effect because they only give incentives to do the easy research in advance and take away incentives to actually collect data and provide service. As noted animal breeder A.E. Freeman said in 2000, ‘Detecting problems isn’t difficult. Neither is coming up with ideas. After all, ideas are very cheap. What it all comes down to in the end is finding solutions. And it’s often this last bit that’s so difficult’. Organizations often claim that their patents are purely defensive, to let them continue using their own methods. All other organizations have similar fears that their operations could be shut down by a patent, and can never be sure that the holder of a defensive patent might suddenly become offensive. Researchers in dairy cattle breeding now enjoy the very open exchange of ideas and methods necessary for international cooperation in genetic evaluation. Patent disputes can waste much time and money. Without intellectual property, we can continue to share ideas, to invest in data collection, and to make even faster progress.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".