Bibliographic record
Abstract
Fundamentals, advantages, and disadvantages of linked data structures. Moving a linked data structure in memory, or to/from a disk, or transmitting it across a communication channel - techniques of compaction and serialization. Memory allocation from a specific arena. Linked data structures are intimately related to memory, where they are created, located, and processed. Naturally for this book, this relationship with memory is our principal focus in this chapter. It is not our intention to provide a comprehensive presentation of linked data structures in C or C++ and their applications. There are many excellent books on the topic of algorithms and data structures, particularly in C and C++. Linked data structures and their applications are one of the great successes of the early decades of computer science. The reader can easily imagine how useful software would be if it worked solely with numbers and arrays. But how then would we model and program such things as lists, graphs, stacks, queues, charts, diagrams, and many other abstract notions that are needed to deal with today's complex programming tasks? Philosophically, a linked approach to data structures is used for any of the following reasons: the data structure must be created dynamically; each part of it is created at a different time; the mutual relations of the parts change in time. Sometimes links are used as a “physical” implementation of “logical relations” (the best example would be databases and the use of foreign keys as “links”).
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.020 |
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".