Bibliographic record
Abstract
Why is what we call a fish so confusing? It seems to me that sometimes in the sciences, as much as we try to be descriptive, precise, direct, clear, and accurate, sometimes we don't always say exactly what we mean. When we interact with the broader public and their more common vernacular (and common names for organisms), forget it. What we say and what we mean and what others think we mean can get pretty discombobulated pretty fast. How can we get back to being combobulated when there is so much confusion over what exactly is a fish, let alone how we describe their characteristics? This is all true for those of us working in the fisheries profession. Did you know that silverfish are actually an insect? Or that jellyfish are actually a gooey invertebrate (mainly Cnidarians or else Ctenophores or Siphonophores [and even here, we use extra “C's” that are silent to confuse things]). Or that shellfish are really just mollusks, unless they are crustaceans, but neither are really fish. Or that starfish are actually, well, a starfish, or more technically asteroids (as in an echinoderm, not the rocky space debris hiding the Millenium Falcon). Or that cuttlefish are basically squids with a Napoleon complex. And that dolphins are not actually a fish, unless you mean dolphinfish (Coryphaenids). So maybe some of those are common language holdovers from sailors in centuries past who classified just about anything from the ocean that moved as a type of fish (except for seals, whom they called mermaids, but that's another column entirely). Still, those names have stuck around even if a bit taxonomically challenged. And let's think about fish color for a moment. Take red-fish, generically speaking. Fish such as various species of Sebastes or Lutjanus or Sciaenops or in the Trachichthyidae family. There are numerous species among those fish that have some variant of “red” in their common name. But when you look at them, at least a third or more are actually not red. And the Orange Roughy Hoplostethus atlanticus are actually more redish, just saying. At least blue-fish are blue, except for then they're not and they're more silverish or greenish-yellow. And the Yellowbar Angelfish Pomacanthus maculosus is actually mostly blue. There sure does seem to be a lot of confusion, or perhaps even color-blindness. Thank Carl Linnaeus (the patron saint of taxonomists, college Botany, and 9th grade Latin) that we have the Integrated Taxonomic Information System (ITIS; https://www.itis.gov/), a cadre of professional taxonomists and ichthyologists, and especially the AFS Committee on Names of Fishes which periodically produces the book, Common and Scientific Names of Fishes from the United States, Canada, and Mexico. Though I am sure that they, and hopefully nearly all readers of Fisheries magazine, can tell you what a fish truly is, we still seem to have some challenges when it comes to common names. Despite the definitive clarity that the binomial nomenclature these professional namers and classifiers have given us for scientific names of fishes, that hasn't always caught on and is often difficult to translate to the general public we have to deal with. Hence, confusion remains. So, what does it matter? Ummm, probably not that much. I bet you didn't expect that plot twist (if there can be such a thing in a ~750 word column). At the end of the day, ultimately, who cares what people call fish as long as they call them something? At least fish are not being mostly ignored, unlike silverfish or siphonophores. I mean, binomial nomenclature was designed to avoid confusion, and largely the technical applications in fisheries have used the Latin names to great effect, so us professionals mostly have it figured out when it comes to calling fish particular names… mostly. But there is the formal, technical utility of scientifically naming things, and then there is the common vernacular usage. And that the two don't always agree is not surprising nor really that big of a deal. Dealing with the public, being aware of popular opinion, respecting local traditions, and acknowledging common understanding and terminology may not be technically correct, but dealing with the public and their perception is all part of being a fisheries professional. So, maybe instead of us fisheries pros getting worked up about the specific accuracy of fish names when dealing with those generally interested in fishes, we simply acknowledge that we generally know what they're referring to. Unless we truly need to clarify, or we really just feel like flexing and want to drop an Oncorhynchus mykiss on someone and ask ‘em what color it is. This month's column was brought to you by the TLAA (taxonomic lumpers association of America, not to be confused with a TLA)… until next month.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.048 | 0.001 |
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; both teacher heads agree on what is shown here.
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".