Estimation des concentrations de sédiments en suspension dans le fleuve Saint-Jean (Nouveau-Brunswick) et établissement de liens avec les données météorologiques locales.
Bibliographic record
Abstract
Apoptosis- and proliferation-effector genes are substantially regulated by the same transactivators, with E2F-1 and Oct-1 being notable examples. The larger proliferation-effector genes have more binding sites for the transactivators that regulate both sets of genes, and proliferation-effector genes have more regions of active chromatin, i.e, DNase I hypersensitive and histone 3, lysine-4 trimethylation sites. Thus, the size differences between the 2 classes of genes suggest a transcriptional regulation paradigm whereby the accumulation of transcription factors that regulate both sets of genes, merely as an aspect of stochastic behavior, accumulate first on the larger proliferation-effector gene "traps," and then accumulate on the apoptosis effector genes, thereby effecting sequential activation of the 2 different gene sets. As IRF-1 and p53 levels increase, tumor suppressor proteins are first activated, followed by the activation of apoptosis-effector genes, for example during S-phase pausing for DNA repair. Tumor suppressor genes are larger than apoptosis-effector genes and have more IRF-1 and p53 binding sites, thereby likewise suggesting a paradigm for transcription sequencing based on stochastic interactions of transcription factors with different gene classes. In this report, using the ENCODE database, we determined that tumor suppressor genes have a greater number of open chromatin regions and histone 3 lysine-4 trimethylation sites, consistent with the idea that a larger gene size can facilitate earlier transcriptional activation via the inclusion of more transactivator binding sites.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".