Atlantic Geoscience Society & Environmental Earth Sciences Division of the Geological Association of Canada: Abstracts 2000, Joint Meeting & Conference
Bibliographic record
Abstract
Few natural sources o f iron occur in the deep ocean.Shipwrecks deposited in this environment introduce massive amounts o f processed iron and iron derivatives unnatural to the deep ocean, providing a new source o f nutrients.As a result, microorganisms normally present in low concentrations are provided with a new iron-rich environment in which they can flourish, thereby creating a unique ecosystem.Studies show that both biological and mineralogical activities play a major role in the corrosive process that forms this unique ecosystem.Micro-organisms precipitate iron-rich minerals, which form the brittle skeleton o f stalactite-like structures termed 'rusticles'.The skeleton supports the newly formed structures, preventing them from washing away in local currents.After more than 70 years o f uninterrupted growth, rusticles now cover the hull o f the RMS Titanic.Little is known about the structure o f rusticles or about the microorganisms involved in their formation.The internal and external surfaces o f rusticles differ in their morphology and mineralogy, suggesting that different bacteria and/or physicalchemical conditions prevailed during their formation.It is believed that more than 2 0 different species o f microorganisms can be found in these structures.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.186 | 0.050 |
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".