Fungal diversity of marine biofilms on artificial reefs in the north-central Gulf of Mexico
Bibliographic record
Abstract
Abstract We present the first characterization of fungal community diversity of natural mixed-species biofilms on artificial marine reefs. Four artificial reefs in the Mississippi (MS) Sound, USA, representing low-profile (underwater) and high-profile (periodically air-exposed) conditions were sampled every 3 months over a 23-month period to investigate changes in fungal diversity within reef biofilms. Fungal presence was assessed via PCR amplification of the internal transcribed spacer (ITS) region of fungal ribosomal DNA, and by terminal restriction fragment length polymorphism (T-RFLP) analysis of fungal ITS regions – the latter being used to track variation in fungal community structure with respect to season, location, and reef profile type. Fungal communities were also characterized taxonomically through both morphological identification and phylogenetic comparisons of ITS gene sequences, with 36 fungal genera cultured from reef biofilms. Using a multivariate statistical approach, significant temporal and spatial differences in fungal biofilm communities were detected. High-profile reefs differed significantly in biofilm fungal community composition across the 10 sampling periods. This assessment of marine fungal biofilm communities over time provides novel insights into the fungal diversity present on artificial reefs in an understudied region, the north-central Gulf of Mexico.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".