Bibliographic record
Abstract
The description of ocean water masses is based on the study of their temperature, salinity, and density, virtual genetic imprints which provide identity and movement to water masses. Ocean characteristics and processes involved in exchanges with the atmosphere together with simple dynamic balances give an understanding of a large part of the vast oceanic system. This book is enhanced with numerous colored illustrations. It is a reference on regional oceanography updated with extensive results from the last twenty years. The presentation underscores the specificity of each ocean basin using a precise and global approach. Beginning with a brief historical context, it explains the interactions and the role of each ocean basin in the functioning of the planetary ocean. How do we recognize Antarctic Bottom Water in the middle of the Atlantic Ocean? What is the densest water mass? The warmest? Why doesn't dense water form in the largest ocean basin? What becomes of water that sinks in the Labrador Sea? Why does the ocean play such an important role in climate variations? ? Answers can be found in this book. Beyond a course in regional oceanography, the text is aimed at students in all fields of marine and environmental science as well as interested secondary school teachers. It also provides a guide to exploring the «ocean planet» that is comprehensible to any well-informed amateur eager to know the basics.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.176 | 0.182 |
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".