Fatty acid biomarkers in three key marine species of a Patagonian fjord (Yendegaia Fjord, Chile)
Bibliographic record
Abstract
Fjord ecosystems are located in high latitude environments across Scandinavia, Alaska, Canada, Chile, New Zealand, and some more northerly Antarctic and Arctic environments (Freeland et al. 1980). Fjords are water bodies with variable-depth estuarine features, are highly stratified, and are influenced by tidal currents (Pickard 1961; Wassmann et al. 2000). One of the largest regions of fjords in the world is the Chilean Patagonia (from 41º to 56ºS), stretching over 241,000 Km2 with islands, channels, estuaries, bays and gulfs formed by glacial erosion over the current quaternary (Borgel 1970; Holtedahl 2006; Aracena et al. 2015). This fjord region is characterized by low concentrations of dissolved inorganic nutrients in surface waters, and high concentrations of nutrients supplied by sub-Antarctic ocean waters (Silva et al. 1997, 1998; Silva 2008; González et al. 2011). Stratification in the water column is a barrier that reduces the export of phytoplanktonic carbon and influences the distribution of some zooplankton groups (Ji et al. 2010; González et al. 2011; Tamelander et al. 2012). Fjords are important sites for carbon cycles and biological productivity (Wetzel 2001; González et al. 2006; Pomeroy 2006), and are sensitive to environmental and climatic changes (Overpeck et al. 1997; Svendsen et al. 2002; Whitehead et al. 2009).
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".