Bibliographic record
Abstract
Fundamentals of dynamic allocation and deallocation of memory: free store (system heap); per-process memory manager; C memory allocators malloc(), calloc(), and realloc(); and C deallocator free(). How to handle memory allocation/deallocation errors. In previous chapters we have mentioned dynamic allocation of memory several times. In Chapter 2 we had quite a detailed look at the static allocation when we discussed the process of compilation, linking, loading, and execution of a program. We mentioned dynamic allocation from a general point of view or, to be more precise, from the operating system point of view. The reader should be comfortable with the idea that a running program is allocated several segments - not necessarily contiguous - of memory to which its address space is mapped during program execution. These segments together constitute “the program's memory space”, or the “memory it owns”, where all the program's instructions and data needed for the execution are stored and accessible. It is obvious that many programs need to increase their memory during their execution; for instance, they might need to store more data or even more instructions. As mentioned at the end of Chapter 2, it would be more precise to talk about a process rather than a running program. Modern operating systems like UNIX thus have a process memory manager , software that is responsible for providing additional allocation of memory to its process and for deallocation when the memory is no longer needed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".