Bibliographic record
Abstract
a Abl tyrosine kinase activity, quantitative analysis of 202 abscisic acid (ABA) 260 acetyl-coA carboxylase (ACC) 5 activity-based probe (ABP) 239-241 acute myeloid leukemia (AML) 21 adenomatous polyposis coli (APC) 17 adenosine 5 ′ -diphosphate (ADP) 147 adenosine 5 ′ -[γ-thio] triphosphate (S-ATP) 170, 171 adenosine 5 ′ -triphosphate (ATP) γ-phosphate of 139 γ-phosphoryl of 139 adenosine 5 ′ -triphosphate (ATP) analogs 139 α-and β-phosphate modified 149, 150 -applications 147, 162 -base modified 140 -bumped 67, 75, 129 -C2, C6 and C8-modified 140, 141 γ-phosphate modified 152, 159 -imidazole 146 -N6-modified 141, 142 -pyrazolopyrimidine 145 -radiolabel 70, 74 -sugar-modified 148 -structure 65 -synthetic 70 -triazole 146 -triphosphate-modified 149 adenosine monophosphate-activated protein kinase (AMPK) 227 affinity-purification mass spectrometry (AP-MS) 17 AGC kinases 3 -master regulator of 4 alkaline phosphatase (ALP), dephosphorylation 174 α-and β-phosphate modified ATP analogs 149, 150 α-helix C 53 Alzheimer's disease (AD) 10, 12, 16, 287 AMP-activated protein kinase (AMPK) 5 -activation of 5 AMP-CPP 151 AMP-PCP 150 AMP-PNP 151 amyloid precursor protein (APP) 10 amyloid-beta (Aβ) peptides 288 analog sensitive (AS)-kinase 65, 142, 264 -applications for 68, 147 -inhibitors for 75, 76 -multiple mutated 144 -refinements through use of 70 -specificity of 70 analog sensitive (AS)-kinase substrate identification 70 -in action 73 -phosphoproteomics 69 anti-ferrocene antibodies 158, 187 anticancer agents, multimodal 317 antimalarial drug discovery, protein kinase in 128 apicomplexan protein kinases 129 apoptosis 85, 103 -caspase-dependent pathways 86 -extrinsic 86 -intrinsic 87 apoptosis-inducing factor (AIF) 88 apoptotic death receptor 87 apoptotic induction 91 apoptotic progression 94 apoptotic protease-activating factor 1 (APAF1) 88
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.689 | 0.559 |
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".